├── requirements.txt ├── Image ├── Video.gif ├── audio.gif ├── image.gif ├── AudiotoTD.png ├── Hy3DtoTD.png ├── ImagetoTD.png ├── Manager.png ├── VideotoTD.png ├── LoadTDImage.png ├── PointCloud.gif ├── Tripo3DtoTD.png ├── TripoSRtoTD.png ├── Comfy3DPacktoTD.png └── ImagetoTD(JPEG).png ├── ComfyUI2TD_JISEN_v5.2.8.tox ├── pyproject.toml ├── __init__.py ├── .github └── workflows │ └── publish.yml ├── Workflows ├── Live_Portrait_01.json ├── Tripo3D_PointCloud.json ├── 3DP_Trellis.json ├── 3DP_TripoSR.json ├── 3DP_StableFast3D.json ├── TripoSR_PointCloud.json ├── Text2Image_Base.json ├── 3DP_Hunyuan3D_V2.json ├── Image2Image_Base.json ├── Text2Image_LCM.json ├── Text2Image_Lora.json ├── Image2Image_Lora.json ├── SD_Turbo_img2img.json ├── SD_Turbo_img2img_JPEG.json ├── ConrtolNet_Base.json ├── ConrtolNet_Lora.json ├── Animatediff_Text2Video.json ├── Animatediff_Image2Video_Scribble.json └── Hunyuan3D_PointCloud.json ├── README.md ├── README_zh.md ├── LICENSE └── node.py /requirements.txt: -------------------------------------------------------------------------------- 1 | trimesh 2 | imageio_ffmpeg -------------------------------------------------------------------------------- /Image/Video.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/Video.gif -------------------------------------------------------------------------------- /Image/audio.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/audio.gif -------------------------------------------------------------------------------- /Image/image.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/image.gif -------------------------------------------------------------------------------- /Image/AudiotoTD.png: 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-------------------------------------------------------------------------------- /Image/VideotoTD.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/VideotoTD.png -------------------------------------------------------------------------------- /Image/LoadTDImage.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/LoadTDImage.png -------------------------------------------------------------------------------- /Image/PointCloud.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/PointCloud.gif -------------------------------------------------------------------------------- /Image/Tripo3DtoTD.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/Tripo3DtoTD.png -------------------------------------------------------------------------------- /Image/TripoSRtoTD.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/TripoSRtoTD.png -------------------------------------------------------------------------------- /Image/Comfy3DPacktoTD.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/Comfy3DPacktoTD.png -------------------------------------------------------------------------------- /Image/ImagetoTD(JPEG).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/Image/ImagetoTD(JPEG).png -------------------------------------------------------------------------------- /ComfyUI2TD_JISEN_v5.2.8.tox: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JiSenHua/ComfyUI-TD/HEAD/ComfyUI2TD_JISEN_v5.2.8.tox -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "ComfyUI-TD" 3 | description = "A custom node for ComfyUI designed to facilitate the real-time transmission of rendered images, videos, or 3D models to TouchDesigner." 4 | version = "1.1.2" 5 | license = {file = "LICENSE"} 6 | 7 | [project.urls] 8 | Repository = "https://github.com/JiSenHua/ComfyUI-TD" 9 | # Used by Comfy Registry https://comfyregistry.org 10 | 11 | [tool.comfy] 12 | PublisherId = "jisenhua" 13 | DisplayName = "ComfyUI-TD" 14 | Icon = "" 15 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .node import * 2 | 3 | NODE_CLASS_MAPPINGS = { 4 | "Comfy3DPacktoTD": Comfy3DPacktoTD, 5 | "Hy3DtoTD": Hy3DtoTD, 6 | "ImagetoTD": ImagetoTD, 7 | "ImagetoTD(JPEG)": ImagetoTD_JPEG, 8 | "Tripo3DtoTD": Tripo3DtoTD, 9 | "TripoSRtoTD": TripoSRtoTD, 10 | "LoadTDImage": LoadTDImage, 11 | "VideotoTD": VideotoTD, 12 | "AudiotoTD": AudiotoTD, 13 | } 14 | 15 | NODE_DISPLAY_NAME_MAPPINGS = { 16 | "Comfy3DPacktoTD": "Comfy3DPacktoTD", 17 | "Hy3DtoTD": "Hy3DtoTD", 18 | "ImagetoTD": "ImagetoTD", 19 | "ImagetoTD(JPEG)": "ImagetoTD(JPEG)", 20 | "Tripo3DtoTD": "Tripo3DtoTD", 21 | "TripoSRtoTD": "TripoSRtoTD", 22 | "LoadTDImage": "LoadTDImage", 23 | "VideotoTD": "VideotoTD", 24 | "AudiotoTD": "AudiotoTD", 25 | } 26 | -------------------------------------------------------------------------------- /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | - master 8 | paths: 9 | - "pyproject.toml" 10 | 11 | permissions: 12 | issues: write 13 | 14 | jobs: 15 | publish-node: 16 | name: Publish Custom Node to registry 17 | runs-on: ubuntu-latest 18 | if: ${{ github.repository_owner == 'JiSenHua' }} 19 | steps: 20 | - name: Check out code 21 | uses: actions/checkout@v4 22 | with: 23 | submodules: true 24 | - name: Publish Custom Node 25 | uses: Comfy-Org/publish-node-action@v1 26 | with: 27 | ## Add your own personal access token to your Github Repository secrets and reference it here. 28 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 29 | -------------------------------------------------------------------------------- /Workflows/Live_Portrait_01.json: -------------------------------------------------------------------------------- 1 | { 2 | "14": { 3 | "inputs": { 4 | "rotate_pitch": 0, 5 | "rotate_yaw": 0, 6 | "rotate_roll": 0, 7 | "blink": 0, 8 | "eyebrow": 0, 9 | "wink": 23.5, 10 | "pupil_x": 0, 11 | "pupil_y": 0, 12 | "aaa": 0, 13 | "eee": 0, 14 | "woo": 0, 15 | "smile": 0, 16 | "src_ratio": 1, 17 | "sample_ratio": 1, 18 | "sample_parts": "OnlyExpression", 19 | "crop_factor": 1.7000000000000002, 20 | "src_image": [ 21 | "36", 22 | 0 23 | ] 24 | }, 25 | "class_type": "ExpressionEditor", 26 | "_meta": { 27 | "title": "Expression Editor (PHM)" 28 | } 29 | }, 30 | "36": { 31 | "inputs": { 32 | "image": "" 33 | }, 34 | "class_type": "LoadTDImage", 35 | "_meta": { 36 | "title": "LoadTDImage" 37 | } 38 | }, 39 | "37": { 40 | "inputs": { 41 | "broadcast": false, 42 | "images": [ 43 | "14", 44 | 0 45 | ] 46 | }, 47 | "class_type": "ImagetoTD", 48 | "_meta": { 49 | "title": "ImagetoTD" 50 | } 51 | } 52 | } 53 | -------------------------------------------------------------------------------- /Workflows/Tripo3D_PointCloud.json: -------------------------------------------------------------------------------- 1 | { 2 | "18": { 3 | "inputs": { 4 | "mode": "image_to_model", 5 | "apikey": "", 6 | "prompt": "a Pikachu model", 7 | "model_version": "v2.5-20250123", 8 | "style": "None", 9 | "texture": true, 10 | "pbr": true, 11 | "image_seed": 42, 12 | "model_seed": 42, 13 | "texture_seed": 42, 14 | "texture_quality": "standard", 15 | "texture_alignment": "original_image", 16 | "face_limit": -1, 17 | "quad": false, 18 | "image": [ 19 | "28", 20 | 0 21 | ] 22 | }, 23 | "class_type": "TripoAPIDraft", 24 | "_meta": { 25 | "title": "Tripo: Generate model" 26 | } 27 | }, 28 | "24": { 29 | "inputs": { 30 | "model_file": [ 31 | "18", 32 | 0 33 | ], 34 | "broadcast": false 35 | }, 36 | "class_type": "Tripo3DtoTD", 37 | "_meta": { 38 | "title": "Tripo3DtoTD" 39 | } 40 | }, 41 | "28": { 42 | "inputs": { 43 | "image": "" 44 | }, 45 | "class_type": "LoadTDImage", 46 | "_meta": { 47 | "title": "LoadTDImage" 48 | } 49 | } 50 | } 51 | -------------------------------------------------------------------------------- /Workflows/3DP_Trellis.json: -------------------------------------------------------------------------------- 1 | { 2 | "4": { 3 | "inputs": { 4 | "mask": [ 5 | "21", 6 | 1 7 | ] 8 | }, 9 | "class_type": "InvertMask", 10 | "_meta": { 11 | "title": "反转遮罩" 12 | } 13 | }, 14 | "9": { 15 | "inputs": { 16 | "repo_id": "JeffreyXiang/TRELLIS-image-large" 17 | }, 18 | "class_type": "[Comfy3D] Load Trellis Structured 3D Latents Models", 19 | "_meta": { 20 | "title": "Load Trellis Structured 3D Latents Models" 21 | } 22 | }, 23 | "10": { 24 | "inputs": { 25 | "seed": 82615315223344, 26 | "sparse_structure_guidance_scale": 7.5, 27 | "sparse_structure_sample_steps": 12, 28 | "structured_latent_guidance_scale": 3, 29 | "structured_latent_sample_steps": 12, 30 | "trellis_pipe": [ 31 | "9", 32 | 0 33 | ], 34 | "reference_image": [ 35 | "21", 36 | 0 37 | ], 38 | "reference_mask": [ 39 | "4", 40 | 0 41 | ] 42 | }, 43 | "class_type": "[Comfy3D] Trellis Structured 3D Latents Models", 44 | "_meta": { 45 | "title": "Trellis Structured 3D Latents Models" 46 | } 47 | }, 48 | "21": { 49 | "inputs": { 50 | "image": "" 51 | }, 52 | "class_type": "LoadTDImage", 53 | "_meta": { 54 | "title": "LoadTDImage" 55 | } 56 | }, 57 | "22": { 58 | "inputs": { 59 | "broadcast": false, 60 | "mesh": [ 61 | "10", 62 | 0 63 | ] 64 | }, 65 | "class_type": "Comfy3DPacktoTD", 66 | "_meta": { 67 | "title": "Comfy3DPacktoTD" 68 | } 69 | } 70 | } 71 | -------------------------------------------------------------------------------- /Workflows/3DP_TripoSR.json: -------------------------------------------------------------------------------- 1 | { 2 | "20": { 3 | "inputs": { 4 | "geometry_extract_resolution": 256, 5 | "marching_cude_threshold": 25, 6 | "tsr_model": [ 7 | "21", 8 | 0 9 | ], 10 | "reference_image": [ 11 | "27", 12 | 0 13 | ], 14 | "reference_mask": [ 15 | "22", 16 | 0 17 | ] 18 | }, 19 | "class_type": "[Comfy3D] TripoSR", 20 | "_meta": { 21 | "title": "TripoSR" 22 | } 23 | }, 24 | "21": { 25 | "inputs": { 26 | "model_name": "model.ckpt", 27 | "chunk_size": 8192 28 | }, 29 | "class_type": "[Comfy3D] Load TripoSR Model", 30 | "_meta": { 31 | "title": "Load TripoSR Model" 32 | } 33 | }, 34 | "22": { 35 | "inputs": { 36 | "mask": [ 37 | "27", 38 | 1 39 | ] 40 | }, 41 | "class_type": "InvertMask", 42 | "_meta": { 43 | "title": "反转遮罩" 44 | } 45 | }, 46 | "25": { 47 | "inputs": { 48 | "axis_x_to": "+y", 49 | "axis_y_to": "+z", 50 | "axis_z_to": "+x", 51 | "flip_normal": false, 52 | "scale": 1, 53 | "mesh": [ 54 | "20", 55 | 0 56 | ] 57 | }, 58 | "class_type": "[Comfy3D] Switch Mesh Axis", 59 | "_meta": { 60 | "title": "Switch Mesh Axis" 61 | } 62 | }, 63 | "26": { 64 | "inputs": { 65 | "broadcast": false, 66 | "mesh": [ 67 | "25", 68 | 0 69 | ] 70 | }, 71 | "class_type": "Comfy3DPacktoTD", 72 | "_meta": { 73 | "title": "Comfy3DPacktoTD" 74 | } 75 | }, 76 | "27": { 77 | "inputs": { 78 | "image": "" 79 | }, 80 | "class_type": "LoadTDImage", 81 | "_meta": { 82 | "title": "LoadTDImage" 83 | } 84 | } 85 | } 86 | -------------------------------------------------------------------------------- /Workflows/3DP_StableFast3D.json: -------------------------------------------------------------------------------- 1 | { 2 | "20": { 3 | "inputs": { 4 | "mask": [ 5 | "44", 6 | 1 7 | ] 8 | }, 9 | "class_type": "InvertMask", 10 | "_meta": { 11 | "title": "反转遮罩" 12 | } 13 | }, 14 | "36": { 15 | "inputs": { 16 | "model_name": "model.safetensors" 17 | }, 18 | "class_type": "[Comfy3D] Load SF3D Model", 19 | "_meta": { 20 | "title": "Load SF3D Model" 21 | } 22 | }, 23 | "37": { 24 | "inputs": { 25 | "texture_resolution": 1024, 26 | "remesh_option": "None", 27 | "sf3d_model": [ 28 | "36", 29 | 0 30 | ], 31 | "reference_image": [ 32 | "40", 33 | 0 34 | ], 35 | "reference_mask": [ 36 | "40", 37 | 1 38 | ] 39 | }, 40 | "class_type": "[Comfy3D] StableFast3D", 41 | "_meta": { 42 | "title": "StableFast3D" 43 | } 44 | }, 45 | "40": { 46 | "inputs": { 47 | "foreground_ratio": 0.85, 48 | "images": [ 49 | "44", 50 | 0 51 | ], 52 | "masks": [ 53 | "20", 54 | 0 55 | ] 56 | }, 57 | "class_type": "[Comfy3D] Resize Image Foreground", 58 | "_meta": { 59 | "title": "Resize Image Foreground" 60 | } 61 | }, 62 | "43": { 63 | "inputs": { 64 | "axis_x_to": "-x", 65 | "axis_y_to": "+y", 66 | "axis_z_to": "-z", 67 | "flip_normal": false, 68 | "scale": 1, 69 | "mesh": [ 70 | "37", 71 | 0 72 | ] 73 | }, 74 | "class_type": "[Comfy3D] Switch Mesh Axis", 75 | "_meta": { 76 | "title": "Switch Mesh Axis" 77 | } 78 | }, 79 | "44": { 80 | "inputs": { 81 | "image": "" 82 | }, 83 | "class_type": "LoadTDImage", 84 | "_meta": { 85 | "title": "LoadTDImage" 86 | } 87 | }, 88 | "45": { 89 | "inputs": { 90 | "broadcast": false, 91 | "mesh": [ 92 | "43", 93 | 0 94 | ] 95 | }, 96 | "class_type": "Comfy3DPacktoTD", 97 | "_meta": { 98 | "title": "Comfy3DPacktoTD" 99 | } 100 | } 101 | } 102 | -------------------------------------------------------------------------------- /Workflows/TripoSR_PointCloud.json: -------------------------------------------------------------------------------- 1 | { 2 | "12": { 3 | "inputs": { 4 | "geometry_resolution": 256, 5 | "threshold": 25, 6 | "model": [ 7 | "14", 8 | 0 9 | ], 10 | "reference_image": [ 11 | "33", 12 | 0 13 | ], 14 | "reference_mask": [ 15 | "33", 16 | 1 17 | ] 18 | }, 19 | "class_type": "TripoSRSampler", 20 | "_meta": { 21 | "title": "TripoSR Sampler" 22 | } 23 | }, 24 | "14": { 25 | "inputs": { 26 | "model": "TripoSRmodel.ckpt", 27 | "chunk_size": 8192 28 | }, 29 | "class_type": "TripoSRModelLoader", 30 | "_meta": { 31 | "title": "TripoSR Model Loader" 32 | } 33 | }, 34 | "29": { 35 | "inputs": { 36 | "image": [ 37 | "34", 38 | 0 39 | ] 40 | }, 41 | "class_type": "SplitImageWithAlpha", 42 | "_meta": { 43 | "title": "分离图像Alpha" 44 | } 45 | }, 46 | "32": { 47 | "inputs": { 48 | "model": "u2net: general purpose", 49 | "providers": "CUDA" 50 | }, 51 | "class_type": "RemBGSession+", 52 | "_meta": { 53 | "title": "🔧 RemBG Session" 54 | } 55 | }, 56 | "33": { 57 | "inputs": { 58 | "rembg_session": [ 59 | "32", 60 | 0 61 | ], 62 | "image": [ 63 | "29", 64 | 0 65 | ] 66 | }, 67 | "class_type": "ImageRemoveBackground+", 68 | "_meta": { 69 | "title": "🔧 Image Remove Background" 70 | } 71 | }, 72 | "34": { 73 | "inputs": { 74 | "width": 512, 75 | "height": 512, 76 | "interpolation": "nearest", 77 | "method": "pad", 78 | "condition": "always", 79 | "multiple_of": 0, 80 | "image": [ 81 | "37", 82 | 0 83 | ] 84 | }, 85 | "class_type": "ImageResize+", 86 | "_meta": { 87 | "title": "🔧 Image Resize" 88 | } 89 | }, 90 | "37": { 91 | "inputs": { 92 | "image": "" 93 | }, 94 | "class_type": "LoadTDImage", 95 | "_meta": { 96 | "title": "LoadTDImage" 97 | } 98 | }, 99 | "38": { 100 | "inputs": { 101 | "broadcast": false, 102 | "mesh": [ 103 | "12", 104 | 0 105 | ] 106 | }, 107 | "class_type": "TripoSRtoTD", 108 | "_meta": { 109 | "title": "TripoSRtoTD" 110 | } 111 | } 112 | } -------------------------------------------------------------------------------- /Workflows/Text2Image_Base.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "ckpt_name": "SD1.5_dreamshaper_8.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Checkpoint 模型加载" 9 | } 10 | }, 11 | "3": { 12 | "inputs": { 13 | "width": 512, 14 | "height": 512, 15 | "batch_size": 1 16 | }, 17 | "class_type": "EmptyLatentImage", 18 | "_meta": { 19 | "title": "Latent 潜空间(图像)大小" 20 | } 21 | }, 22 | "4": { 23 | "inputs": { 24 | "text": "cat, bed, realistic, high quality", 25 | "clip": [ 26 | "1", 27 | 1 28 | ] 29 | }, 30 | "class_type": "CLIPTextEncode", 31 | "_meta": { 32 | "title": "CLIP文本编码器 正向提示词" 33 | } 34 | }, 35 | "5": { 36 | "inputs": { 37 | "text": "bad quality, bad anatomy, worst quality, low quality, low resolution, extra fingers, blur, blurry, ugly, wrong proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image, deformation, skin moles", 38 | "clip": [ 39 | "1", 40 | 1 41 | ] 42 | }, 43 | "class_type": "CLIPTextEncode", 44 | "_meta": { 45 | "title": "CLIP文本编码器 负向提示词" 46 | } 47 | }, 48 | "6": { 49 | "inputs": { 50 | "seed": 0, 51 | "steps": 20, 52 | "cfg": 8, 53 | "sampler_name": "euler", 54 | "scheduler": "normal", 55 | "denoise": 1, 56 | "model": [ 57 | "1", 58 | 0 59 | ], 60 | "positive": [ 61 | "4", 62 | 0 63 | ], 64 | "negative": [ 65 | "5", 66 | 0 67 | ], 68 | "latent_image": [ 69 | "3", 70 | 0 71 | ] 72 | }, 73 | "class_type": "KSampler", 74 | "_meta": { 75 | "title": "Ksampler 采样器" 76 | } 77 | }, 78 | "8": { 79 | "inputs": { 80 | "samples": [ 81 | "6", 82 | 0 83 | ], 84 | "vae": [ 85 | "1", 86 | 2 87 | ] 88 | }, 89 | "class_type": "VAEDecode", 90 | "_meta": { 91 | "title": "VAE解码" 92 | } 93 | }, 94 | "9": { 95 | "inputs": { 96 | "broadcast": false, 97 | "images": [ 98 | "8", 99 | 0 100 | ] 101 | }, 102 | "class_type": "ImagetoTD", 103 | "_meta": { 104 | "title": "ImagetoTD" 105 | } 106 | } 107 | } 108 | -------------------------------------------------------------------------------- /Workflows/3DP_Hunyuan3D_V2.json: -------------------------------------------------------------------------------- 1 | { 2 | "10": { 3 | "inputs": { 4 | "diffusers_pipeline_name": "Hunyuan3DDiTFlowMatchingPipeline", 5 | "repo_id": "tencent/Hunyuan3D-2", 6 | "custom_pipeline": "", 7 | "force_download": false, 8 | "checkpoint_sub_dir": "" 9 | }, 10 | "class_type": "[Comfy3D] Load Diffusers Pipeline", 11 | "_meta": { 12 | "title": "Load Diffusers Pipeline" 13 | } 14 | }, 15 | "11": { 16 | "inputs": { 17 | "diffusers_pipeline_name": "Hunyuan3DPaintPipeline", 18 | "repo_id": "tencent/Hunyuan3D-2", 19 | "custom_pipeline": "", 20 | "force_download": false, 21 | "checkpoint_sub_dir": "" 22 | }, 23 | "class_type": "[Comfy3D] Load Diffusers Pipeline", 24 | "_meta": { 25 | "title": "Load Diffusers Pipeline" 26 | } 27 | }, 28 | "12": { 29 | "inputs": { 30 | "seed": 1234, 31 | "guidance_scale": 5.5, 32 | "num_inference_steps": 30, 33 | "octree_resolution": 256, 34 | "hunyuan3d_v2_i23d_pipe": [ 35 | "10", 36 | 0 37 | ], 38 | "reference_image": [ 39 | "23", 40 | 0 41 | ], 42 | "reference_mask": [ 43 | "14", 44 | 0 45 | ] 46 | }, 47 | "class_type": "[Comfy3D] Hunyuan3D V2 DiT Flow Matching Model", 48 | "_meta": { 49 | "title": "Hunyuan3D V2 DiT Flow Matching Model" 50 | } 51 | }, 52 | "14": { 53 | "inputs": { 54 | "mask": [ 55 | "23", 56 | 1 57 | ] 58 | }, 59 | "class_type": "InvertMask", 60 | "_meta": { 61 | "title": "反转遮罩" 62 | } 63 | }, 64 | "17": { 65 | "inputs": { 66 | "hunyuan3d_v2_texgen_pipe": [ 67 | "11", 68 | 0 69 | ], 70 | "reference_image": [ 71 | "23", 72 | 0 73 | ], 74 | "reference_mask": [ 75 | "14", 76 | 0 77 | ], 78 | "mesh": [ 79 | "12", 80 | 0 81 | ] 82 | }, 83 | "class_type": "[Comfy3D] Hunyuan3D V2 Paint Model", 84 | "_meta": { 85 | "title": "Hunyuan3D V2 Paint Model" 86 | } 87 | }, 88 | "23": { 89 | "inputs": { 90 | "image": "" 91 | }, 92 | "class_type": "LoadTDImage", 93 | "_meta": { 94 | "title": "LoadTDImage" 95 | } 96 | }, 97 | "24": { 98 | "inputs": { 99 | "broadcast": false, 100 | "mesh": [ 101 | "17", 102 | 0 103 | ] 104 | }, 105 | "class_type": "Comfy3DPacktoTD", 106 | "_meta": { 107 | "title": "Comfy3DPacktoTD" 108 | } 109 | } 110 | } 111 | -------------------------------------------------------------------------------- /Workflows/Image2Image_Base.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "ckpt_name": "SD1.5_dreamshaper_8.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Checkpoint 模型加载" 9 | } 10 | }, 11 | "4": { 12 | "inputs": { 13 | "text": "man, Comic style, hight quality", 14 | "clip": [ 15 | "1", 16 | 1 17 | ] 18 | }, 19 | "class_type": "CLIPTextEncode", 20 | "_meta": { 21 | "title": "CLIP文本编码器 正向提示词" 22 | } 23 | }, 24 | "5": { 25 | "inputs": { 26 | "text": "bad quality, bad anatomy, worst quality, low quality, low resolution, extra fingers, blur, blurry, ugly, wrong proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image, deformation, skin moles", 27 | "clip": [ 28 | "1", 29 | 1 30 | ] 31 | }, 32 | "class_type": "CLIPTextEncode", 33 | "_meta": { 34 | "title": "CLIP文本编码器 负向提示词" 35 | } 36 | }, 37 | "6": { 38 | "inputs": { 39 | "seed": 0, 40 | "steps": 20, 41 | "cfg": 8, 42 | "sampler_name": "euler", 43 | "scheduler": "normal", 44 | "denoise": 0.6, 45 | "model": [ 46 | "1", 47 | 0 48 | ], 49 | "positive": [ 50 | "4", 51 | 0 52 | ], 53 | "negative": [ 54 | "5", 55 | 0 56 | ], 57 | "latent_image": [ 58 | "8", 59 | 0 60 | ] 61 | }, 62 | "class_type": "KSampler", 63 | "_meta": { 64 | "title": "Ksampler 采样器" 65 | } 66 | }, 67 | "8": { 68 | "inputs": { 69 | "pixels": [ 70 | "10", 71 | 0 72 | ], 73 | "vae": [ 74 | "1", 75 | 2 76 | ] 77 | }, 78 | "class_type": "VAEEncode", 79 | "_meta": { 80 | "title": "VAE编码" 81 | } 82 | }, 83 | "9": { 84 | "inputs": { 85 | "samples": [ 86 | "6", 87 | 0 88 | ], 89 | "vae": [ 90 | "1", 91 | 2 92 | ] 93 | }, 94 | "class_type": "VAEDecode", 95 | "_meta": { 96 | "title": "VAE解码" 97 | } 98 | }, 99 | "10": { 100 | "inputs": { 101 | "image": "" 102 | }, 103 | "class_type": "LoadTDImage", 104 | "_meta": { 105 | "title": "LoadTDImage" 106 | } 107 | }, 108 | "11": { 109 | "inputs": { 110 | "broadcast": false, 111 | "images": [ 112 | "9", 113 | 0 114 | ] 115 | }, 116 | "class_type": "ImagetoTD", 117 | "_meta": { 118 | "title": "ImagetoTD" 119 | } 120 | } 121 | } 122 | -------------------------------------------------------------------------------- /Workflows/Text2Image_LCM.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "ckpt_name": "SD1.5_dreamshaper_8.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Checkpoint 模型加载" 9 | } 10 | }, 11 | "3": { 12 | "inputs": { 13 | "width": 512, 14 | "height": 512, 15 | "batch_size": 1 16 | }, 17 | "class_type": "EmptyLatentImage", 18 | "_meta": { 19 | "title": "Latent 潜空间(图像)大小" 20 | } 21 | }, 22 | "4": { 23 | "inputs": { 24 | "text": "cat, bed, realistic, high quality", 25 | "clip": [ 26 | "1", 27 | 1 28 | ] 29 | }, 30 | "class_type": "CLIPTextEncode", 31 | "_meta": { 32 | "title": "CLIP文本编码器 正向提示词" 33 | } 34 | }, 35 | "5": { 36 | "inputs": { 37 | "text": "bad quality, bad anatomy, worst quality, low quality, low resolution, extra fingers, blur, blurry, ugly, wrong proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image, deformation, skin moles", 38 | "clip": [ 39 | "1", 40 | 1 41 | ] 42 | }, 43 | "class_type": "CLIPTextEncode", 44 | "_meta": { 45 | "title": "CLIP文本编码器 负向提示词" 46 | } 47 | }, 48 | "6": { 49 | "inputs": { 50 | "seed": 0, 51 | "steps": 3, 52 | "cfg": 1.5, 53 | "sampler_name": "lcm", 54 | "scheduler": "sgm_uniform", 55 | "denoise": 1, 56 | "model": [ 57 | "9", 58 | 0 59 | ], 60 | "positive": [ 61 | "4", 62 | 0 63 | ], 64 | "negative": [ 65 | "5", 66 | 0 67 | ], 68 | "latent_image": [ 69 | "3", 70 | 0 71 | ] 72 | }, 73 | "class_type": "KSampler", 74 | "_meta": { 75 | "title": "Ksampler 采样器" 76 | } 77 | }, 78 | "8": { 79 | "inputs": { 80 | "samples": [ 81 | "6", 82 | 0 83 | ], 84 | "vae": [ 85 | "1", 86 | 2 87 | ] 88 | }, 89 | "class_type": "VAEDecode", 90 | "_meta": { 91 | "title": "VAE解码" 92 | } 93 | }, 94 | "9": { 95 | "inputs": { 96 | "lora_name": "lcm_lora_sd15.safetensors", 97 | "strength_model": 1, 98 | "model": [ 99 | "1", 100 | 0 101 | ] 102 | }, 103 | "class_type": "LoraLoaderModelOnly", 104 | "_meta": { 105 | "title": "LoraLoaderModelOnly" 106 | } 107 | }, 108 | "10": { 109 | "inputs": { 110 | "broadcast": false, 111 | "images": [ 112 | "8", 113 | 0 114 | ] 115 | }, 116 | "class_type": "ImagetoTD", 117 | "_meta": { 118 | "title": "ImagetoTD" 119 | } 120 | } 121 | } 122 | -------------------------------------------------------------------------------- /Workflows/Text2Image_Lora.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "ckpt_name": "SD1.5_dreamshaper_8.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Checkpoint 模型加载" 9 | } 10 | }, 11 | "2": { 12 | "inputs": { 13 | "lora_name": "SD1.5_(增加细节)add_detail.safetensors", 14 | "strength_model": 1, 15 | "strength_clip": 1, 16 | "model": [ 17 | "1", 18 | 0 19 | ], 20 | "clip": [ 21 | "1", 22 | 1 23 | ] 24 | }, 25 | "class_type": "LoraLoader", 26 | "_meta": { 27 | "title": "LoRA 模型加载" 28 | } 29 | }, 30 | "3": { 31 | "inputs": { 32 | "width": 512, 33 | "height": 512, 34 | "batch_size": 1 35 | }, 36 | "class_type": "EmptyLatentImage", 37 | "_meta": { 38 | "title": "Latent 潜空间(图像)大小" 39 | } 40 | }, 41 | "4": { 42 | "inputs": { 43 | "text": "cat, bed, realistic, high quality", 44 | "clip": [ 45 | "2", 46 | 1 47 | ] 48 | }, 49 | "class_type": "CLIPTextEncode", 50 | "_meta": { 51 | "title": "CLIP文本编码器 正向提示词" 52 | } 53 | }, 54 | "5": { 55 | "inputs": { 56 | "text": "bad quality, bad anatomy, worst quality, low quality, low resolution, extra fingers, blur, blurry, ugly, wrong proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image, deformation, skin moles", 57 | "clip": [ 58 | "2", 59 | 1 60 | ] 61 | }, 62 | "class_type": "CLIPTextEncode", 63 | "_meta": { 64 | "title": "CLIP文本编码器 负向提示词" 65 | } 66 | }, 67 | "6": { 68 | "inputs": { 69 | "seed": 0, 70 | "steps": 20, 71 | "cfg": 8, 72 | "sampler_name": "euler", 73 | "scheduler": "normal", 74 | "denoise": 1, 75 | "model": [ 76 | "2", 77 | 0 78 | ], 79 | "positive": [ 80 | "4", 81 | 0 82 | ], 83 | "negative": [ 84 | "5", 85 | 0 86 | ], 87 | "latent_image": [ 88 | "3", 89 | 0 90 | ] 91 | }, 92 | "class_type": "KSampler", 93 | "_meta": { 94 | "title": "Ksampler 采样器" 95 | } 96 | }, 97 | "8": { 98 | "inputs": { 99 | "samples": [ 100 | "6", 101 | 0 102 | ], 103 | "vae": [ 104 | "1", 105 | 2 106 | ] 107 | }, 108 | "class_type": "VAEDecode", 109 | "_meta": { 110 | "title": "VAE解码" 111 | } 112 | }, 113 | "9": { 114 | "inputs": { 115 | "broadcast": false, 116 | "images": [ 117 | "8", 118 | 0 119 | ] 120 | }, 121 | "class_type": "ImagetoTD", 122 | "_meta": { 123 | "title": "ImagetoTD" 124 | } 125 | } 126 | } 127 | -------------------------------------------------------------------------------- /Workflows/Image2Image_Lora.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "ckpt_name": "SD1.5_dreamshaper_8.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Checkpoint 模型加载" 9 | } 10 | }, 11 | "2": { 12 | "inputs": { 13 | "lora_name": "SD1.5_(增加细节)add_detail.safetensors", 14 | "strength_model": 1, 15 | "strength_clip": 1, 16 | "model": [ 17 | "1", 18 | 0 19 | ], 20 | "clip": [ 21 | "1", 22 | 1 23 | ] 24 | }, 25 | "class_type": "LoraLoader", 26 | "_meta": { 27 | "title": "LoRA 模型加载" 28 | } 29 | }, 30 | "4": { 31 | "inputs": { 32 | "text": "man, Comic style, hight quality", 33 | "clip": [ 34 | "2", 35 | 1 36 | ] 37 | }, 38 | "class_type": "CLIPTextEncode", 39 | "_meta": { 40 | "title": "CLIP文本编码器 正向提示词" 41 | } 42 | }, 43 | "5": { 44 | "inputs": { 45 | "text": "bad quality, bad anatomy, worst quality, low quality, low resolution, extra fingers, blur, blurry, ugly, wrong proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image, deformation, skin moles", 46 | "clip": [ 47 | "2", 48 | 1 49 | ] 50 | }, 51 | "class_type": "CLIPTextEncode", 52 | "_meta": { 53 | "title": "CLIP文本编码器 负向提示词" 54 | } 55 | }, 56 | "6": { 57 | "inputs": { 58 | "seed": 0, 59 | "steps": 20, 60 | "cfg": 8, 61 | "sampler_name": "euler", 62 | "scheduler": "normal", 63 | "denoise": 0.6, 64 | "model": [ 65 | "2", 66 | 0 67 | ], 68 | "positive": [ 69 | "4", 70 | 0 71 | ], 72 | "negative": [ 73 | "5", 74 | 0 75 | ], 76 | "latent_image": [ 77 | "8", 78 | 0 79 | ] 80 | }, 81 | "class_type": "KSampler", 82 | "_meta": { 83 | "title": "Ksampler 采样器" 84 | } 85 | }, 86 | "8": { 87 | "inputs": { 88 | "pixels": [ 89 | "11", 90 | 0 91 | ], 92 | "vae": [ 93 | "1", 94 | 2 95 | ] 96 | }, 97 | "class_type": "VAEEncode", 98 | "_meta": { 99 | "title": "VAE编码" 100 | } 101 | }, 102 | "9": { 103 | "inputs": { 104 | "samples": [ 105 | "6", 106 | 0 107 | ], 108 | "vae": [ 109 | "1", 110 | 2 111 | ] 112 | }, 113 | "class_type": "VAEDecode", 114 | "_meta": { 115 | "title": "VAE解码" 116 | } 117 | }, 118 | "10": { 119 | "inputs": { 120 | "broadcast": false, 121 | "images": [ 122 | "9", 123 | 0 124 | ] 125 | }, 126 | "class_type": "ImagetoTD", 127 | "_meta": { 128 | "title": "ImagetoTD" 129 | } 130 | }, 131 | "11": { 132 | "inputs": { 133 | "image": "" 134 | }, 135 | "class_type": "LoadTDImage", 136 | "_meta": { 137 | "title": "LoadTDImage" 138 | } 139 | } 140 | } 141 | -------------------------------------------------------------------------------- /Workflows/SD_Turbo_img2img.json: -------------------------------------------------------------------------------- 1 | { 2 | "6": { 3 | "inputs": { 4 | "text": "1girl, adult woman, teal eyes, natural blonde wavy hair, detailed background, detailed face, aloha shirt, tropical forest in background, palm leaves", 5 | "clip": [ 6 | "20", 7 | 1 8 | ] 9 | }, 10 | "class_type": "CLIPTextEncode", 11 | "_meta": { 12 | "title": "CLIP文本编码" 13 | } 14 | }, 15 | "7": { 16 | "inputs": { 17 | "text": "text, watermark", 18 | "clip": [ 19 | "20", 20 | 1 21 | ] 22 | }, 23 | "class_type": "CLIPTextEncode", 24 | "_meta": { 25 | "title": "CLIP文本编码" 26 | } 27 | }, 28 | "8": { 29 | "inputs": { 30 | "samples": [ 31 | "13", 32 | 0 33 | ], 34 | "vae": [ 35 | "20", 36 | 2 37 | ] 38 | }, 39 | "class_type": "VAEDecode", 40 | "_meta": { 41 | "title": "VAE解码" 42 | } 43 | }, 44 | "13": { 45 | "inputs": { 46 | "add_noise": true, 47 | "noise_seed": 773454489337963, 48 | "cfg": 1, 49 | "model": [ 50 | "20", 51 | 0 52 | ], 53 | "positive": [ 54 | "6", 55 | 0 56 | ], 57 | "negative": [ 58 | "7", 59 | 0 60 | ], 61 | "sampler": [ 62 | "14", 63 | 0 64 | ], 65 | "sigmas": [ 66 | "22", 67 | 0 68 | ], 69 | "latent_image": [ 70 | "30", 71 | 0 72 | ] 73 | }, 74 | "class_type": "SamplerCustom", 75 | "_meta": { 76 | "title": "自定义采样器" 77 | } 78 | }, 79 | "14": { 80 | "inputs": { 81 | "sampler_name": "euler_ancestral" 82 | }, 83 | "class_type": "KSamplerSelect", 84 | "_meta": { 85 | "title": "K采样器选择" 86 | } 87 | }, 88 | "20": { 89 | "inputs": { 90 | "ckpt_name": "sd_turbo.safetensors" 91 | }, 92 | "class_type": "CheckpointLoaderSimple", 93 | "_meta": { 94 | "title": "Checkpoint加载器（简易）" 95 | } 96 | }, 97 | "22": { 98 | "inputs": { 99 | "steps": 1, 100 | "denoise": 1, 101 | "model": [ 102 | "20", 103 | 0 104 | ] 105 | }, 106 | "class_type": "SDTurboScheduler", 107 | "_meta": { 108 | "title": "SDTurbo调度器" 109 | } 110 | }, 111 | "29": { 112 | "inputs": { 113 | "image": "" 114 | }, 115 | "class_type": "LoadTDImage", 116 | "_meta": { 117 | "title": "LoadTDImage" 118 | } 119 | }, 120 | "30": { 121 | "inputs": { 122 | "pixels": [ 123 | "29", 124 | 0 125 | ], 126 | "vae": [ 127 | "20", 128 | 2 129 | ] 130 | }, 131 | "class_type": "VAEEncode", 132 | "_meta": { 133 | "title": "VAE编码" 134 | } 135 | }, 136 | "31": { 137 | "inputs": { 138 | "broadcast": false, 139 | "images": [ 140 | "8", 141 | 0 142 | ] 143 | }, 144 | "class_type": "ImagetoTD", 145 | "_meta": { 146 | "title": "ImagetoTD" 147 | } 148 | } 149 | } -------------------------------------------------------------------------------- /Workflows/SD_Turbo_img2img_JPEG.json: -------------------------------------------------------------------------------- 1 | { 2 | "6": { 3 | "inputs": { 4 | "text": "1girl, adult woman, teal eyes, natural blonde wavy hair, detailed background, detailed face, aloha shirt, tropical forest in background, palm leaves", 5 | "clip": [ 6 | "20", 7 | 1 8 | ] 9 | }, 10 | "class_type": "CLIPTextEncode", 11 | "_meta": { 12 | "title": "CLIP文本编码" 13 | } 14 | }, 15 | "7": { 16 | "inputs": { 17 | "text": "text, watermark", 18 | "clip": [ 19 | "20", 20 | 1 21 | ] 22 | }, 23 | "class_type": "CLIPTextEncode", 24 | "_meta": { 25 | "title": "CLIP文本编码" 26 | } 27 | }, 28 | "8": { 29 | "inputs": { 30 | "samples": [ 31 | "13", 32 | 0 33 | ], 34 | "vae": [ 35 | "20", 36 | 2 37 | ] 38 | }, 39 | "class_type": "VAEDecode", 40 | "_meta": { 41 | "title": "VAE解码" 42 | } 43 | }, 44 | "13": { 45 | "inputs": { 46 | "add_noise": true, 47 | "noise_seed": 773454489337963, 48 | "cfg": 1, 49 | "model": [ 50 | "20", 51 | 0 52 | ], 53 | "positive": [ 54 | "6", 55 | 0 56 | ], 57 | "negative": [ 58 | "7", 59 | 0 60 | ], 61 | "sampler": [ 62 | "14", 63 | 0 64 | ], 65 | "sigmas": [ 66 | "22", 67 | 0 68 | ], 69 | "latent_image": [ 70 | "30", 71 | 0 72 | ] 73 | }, 74 | "class_type": "SamplerCustom", 75 | "_meta": { 76 | "title": "自定义采样器" 77 | } 78 | }, 79 | "14": { 80 | "inputs": { 81 | "sampler_name": "euler_ancestral" 82 | }, 83 | "class_type": "KSamplerSelect", 84 | "_meta": { 85 | "title": "K采样器选择" 86 | } 87 | }, 88 | "20": { 89 | "inputs": { 90 | "ckpt_name": "sd_turbo.safetensors" 91 | }, 92 | "class_type": "CheckpointLoaderSimple", 93 | "_meta": { 94 | "title": "Checkpoint加载器（简易）" 95 | } 96 | }, 97 | "22": { 98 | "inputs": { 99 | "steps": 1, 100 | "denoise": 1, 101 | "model": [ 102 | "20", 103 | 0 104 | ] 105 | }, 106 | "class_type": "SDTurboScheduler", 107 | "_meta": { 108 | "title": "SDTurbo调度器" 109 | } 110 | }, 111 | "29": { 112 | "inputs": { 113 | "image": "" 114 | }, 115 | "class_type": "LoadTDImage", 116 | "_meta": { 117 | "title": "LoadTDImage" 118 | } 119 | }, 120 | "30": { 121 | "inputs": { 122 | "pixels": [ 123 | "29", 124 | 0 125 | ], 126 | "vae": [ 127 | "20", 128 | 2 129 | ] 130 | }, 131 | "class_type": "VAEEncode", 132 | "_meta": { 133 | "title": "VAE编码" 134 | } 135 | }, 136 | "32": { 137 | "inputs": { 138 | "broadcast": false, 139 | "quality": 95, 140 | "images": [ 141 | "8", 142 | 0 143 | ] 144 | }, 145 | "class_type": "ImagetoTD(JPEG)", 146 | "_meta": { 147 | "title": "ImagetoTD(JPEG)" 148 | } 149 | } 150 | } -------------------------------------------------------------------------------- /Workflows/ConrtolNet_Base.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "ckpt_name": "SD1.5_dreamshaper_8.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Checkpoint 模型加载" 9 | } 10 | }, 11 | "3": { 12 | "inputs": { 13 | "control_net_name": "control_v11p_sd15_canny_fp16.safetensors" 14 | }, 15 | "class_type": "ControlNetLoader", 16 | "_meta": { 17 | "title": "ControlNet 模型加载" 18 | } 19 | }, 20 | "4": { 21 | "inputs": { 22 | "preprocessor": "CannyEdgePreprocessor", 23 | "resolution": 512, 24 | "image": [ 25 | "16", 26 | 0 27 | ] 28 | }, 29 | "class_type": "AIO_Preprocessor", 30 | "_meta": { 31 | "title": "ControlNet Aux预处理器" 32 | } 33 | }, 34 | "5": { 35 | "inputs": { 36 | "strength": 1, 37 | "start_percent": 0, 38 | "end_percent": 1, 39 | "positive": [ 40 | "7", 41 | 0 42 | ], 43 | "negative": [ 44 | "8", 45 | 0 46 | ], 47 | "control_net": [ 48 | "3", 49 | 0 50 | ], 51 | "image": [ 52 | "4", 53 | 0 54 | ] 55 | }, 56 | "class_type": "ControlNetApplyAdvanced", 57 | "_meta": { 58 | "title": "ControlNet应用(高级)" 59 | } 60 | }, 61 | "7": { 62 | "inputs": { 63 | "text": "man, Comic style, hight quality", 64 | "clip": [ 65 | "1", 66 | 1 67 | ] 68 | }, 69 | "class_type": "CLIPTextEncode", 70 | "_meta": { 71 | "title": "CLIP文本编码器 正向提示词" 72 | } 73 | }, 74 | "8": { 75 | "inputs": { 76 | "text": "bad quality, bad anatomy, worst quality, low quality, low resolution, extra fingers, blur, blurry, ugly, wrong proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image, deformation, skin moles", 77 | "clip": [ 78 | "1", 79 | 1 80 | ] 81 | }, 82 | "class_type": "CLIPTextEncode", 83 | "_meta": { 84 | "title": "CLIP文本编码器 负向提示词" 85 | } 86 | }, 87 | "9": { 88 | "inputs": { 89 | "seed": 0, 90 | "steps": 20, 91 | "cfg": 8, 92 | "sampler_name": "euler", 93 | "scheduler": "normal", 94 | "denoise": 0.8, 95 | "model": [ 96 | "1", 97 | 0 98 | ], 99 | "positive": [ 100 | "5", 101 | 0 102 | ], 103 | "negative": [ 104 | "5", 105 | 1 106 | ], 107 | "latent_image": [ 108 | "13", 109 | 0 110 | ] 111 | }, 112 | "class_type": "KSampler", 113 | "_meta": { 114 | "title": "Ksampler 采样器" 115 | } 116 | }, 117 | "13": { 118 | "inputs": { 119 | "pixels": [ 120 | "16", 121 | 0 122 | ], 123 | "vae": [ 124 | "1", 125 | 2 126 | ] 127 | }, 128 | "class_type": "VAEEncode", 129 | "_meta": { 130 | "title": "VAE编码" 131 | } 132 | }, 133 | "14": { 134 | "inputs": { 135 | "samples": [ 136 | "9", 137 | 0 138 | ], 139 | "vae": [ 140 | "1", 141 | 2 142 | ] 143 | }, 144 | "class_type": "VAEDecode", 145 | "_meta": { 146 | "title": "VAE解码" 147 | } 148 | }, 149 | "15": { 150 | "inputs": { 151 | "broadcast": false, 152 | "images": [ 153 | "14", 154 | 0 155 | ] 156 | }, 157 | "class_type": "ImagetoTD", 158 | "_meta": { 159 | "title": "ImagetoTD" 160 | } 161 | }, 162 | "16": { 163 | "inputs": { 164 | "image": "" 165 | }, 166 | "class_type": "LoadTDImage", 167 | "_meta": { 168 | "title": "LoadTDImage" 169 | } 170 | } 171 | } 172 | -------------------------------------------------------------------------------- /Workflows/ConrtolNet_Lora.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "ckpt_name": "SD1.5_dreamshaper_8.safetensors" 5 | }, 6 | "class_type": "CheckpointLoaderSimple", 7 | "_meta": { 8 | "title": "Checkpoint 模型加载" 9 | } 10 | }, 11 | "2": { 12 | "inputs": { 13 | "lora_name": "SD1.5_(增加细节)add_detail.safetensors", 14 | "strength_model": 1, 15 | "strength_clip": 1, 16 | "model": [ 17 | "1", 18 | 0 19 | ], 20 | "clip": [ 21 | "1", 22 | 1 23 | ] 24 | }, 25 | "class_type": "LoraLoader", 26 | "_meta": { 27 | "title": "LoRA 模型加载" 28 | } 29 | }, 30 | "3": { 31 | "inputs": { 32 | "control_net_name": "control_v11p_sd15_canny_fp16.safetensors" 33 | }, 34 | "class_type": "ControlNetLoader", 35 | "_meta": { 36 | "title": "ControlNet 模型加载" 37 | } 38 | }, 39 | "4": { 40 | "inputs": { 41 | "preprocessor": "CannyEdgePreprocessor", 42 | "resolution": 512, 43 | "image": [ 44 | "15", 45 | 0 46 | ] 47 | }, 48 | "class_type": "AIO_Preprocessor", 49 | "_meta": { 50 | "title": "ControlNet Aux预处理器" 51 | } 52 | }, 53 | "5": { 54 | "inputs": { 55 | "strength": 1, 56 | "start_percent": 0, 57 | "end_percent": 1, 58 | "positive": [ 59 | "7", 60 | 0 61 | ], 62 | "negative": [ 63 | "8", 64 | 0 65 | ], 66 | "control_net": [ 67 | "3", 68 | 0 69 | ], 70 | "image": [ 71 | "4", 72 | 0 73 | ] 74 | }, 75 | "class_type": "ControlNetApplyAdvanced", 76 | "_meta": { 77 | "title": "ControlNet应用(高级)" 78 | } 79 | }, 80 | "7": { 81 | "inputs": { 82 | "text": "man, Comic style, hight quality", 83 | "clip": [ 84 | "2", 85 | 1 86 | ] 87 | }, 88 | "class_type": "CLIPTextEncode", 89 | "_meta": { 90 | "title": "CLIP文本编码器 正向提示词" 91 | } 92 | }, 93 | "8": { 94 | "inputs": { 95 | "text": "bad quality, bad anatomy, worst quality, low quality, low resolution, extra fingers, blur, blurry, ugly, wrong proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image, deformation, skin moles", 96 | "clip": [ 97 | "2", 98 | 1 99 | ] 100 | }, 101 | "class_type": "CLIPTextEncode", 102 | "_meta": { 103 | "title": "CLIP文本编码器 负向提示词" 104 | } 105 | }, 106 | "9": { 107 | "inputs": { 108 | "seed": 0, 109 | "steps": 20, 110 | "cfg": 8, 111 | "sampler_name": "euler", 112 | "scheduler": "normal", 113 | "denoise": 0.8, 114 | "model": [ 115 | "2", 116 | 0 117 | ], 118 | "positive": [ 119 | "5", 120 | 0 121 | ], 122 | "negative": [ 123 | "5", 124 | 1 125 | ], 126 | "latent_image": [ 127 | "13", 128 | 0 129 | ] 130 | }, 131 | "class_type": "KSampler", 132 | "_meta": { 133 | "title": "Ksampler 采样器" 134 | } 135 | }, 136 | "13": { 137 | "inputs": { 138 | "pixels": [ 139 | "15", 140 | 0 141 | ], 142 | "vae": [ 143 | "1", 144 | 2 145 | ] 146 | }, 147 | "class_type": "VAEEncode", 148 | "_meta": { 149 | "title": "VAE编码" 150 | } 151 | }, 152 | "14": { 153 | "inputs": { 154 | "samples": [ 155 | "9", 156 | 0 157 | ], 158 | "vae": [ 159 | "1", 160 | 2 161 | ] 162 | }, 163 | "class_type": "VAEDecode", 164 | "_meta": { 165 | "title": "VAE解码" 166 | } 167 | }, 168 | "15": { 169 | "inputs": { 170 | "image": "" 171 | }, 172 | "class_type": "LoadTDImage", 173 | "_meta": { 174 | "title": "LoadTDImage" 175 | } 176 | }, 177 | "16": { 178 | "inputs": { 179 | "broadcast": false, 180 | "images": [ 181 | "14", 182 | 0 183 | ] 184 | }, 185 | "class_type": "ImagetoTD", 186 | "_meta": { 187 | "title": "ImagetoTD" 188 | } 189 | } 190 | } 191 | -------------------------------------------------------------------------------- /Workflows/Animatediff_Text2Video.json: -------------------------------------------------------------------------------- 1 | { 2 | "1": { 3 | "inputs": { 4 | "text": "low quality", 5 | "clip": [ 6 | "4", 7 | 1 8 | ] 9 | }, 10 | "class_type": "CLIPTextEncode", 11 | "_meta": { 12 | "title": "CLIP文本编码器 负向提示词" 13 | } 14 | }, 15 | "2": { 16 | "inputs": { 17 | "samples": [ 18 | "11", 19 | 0 20 | ], 21 | "vae": [ 22 | "4", 23 | 2 24 | ] 25 | }, 26 | "class_type": "VAEDecode", 27 | "_meta": { 28 | "title": "VAE解码" 29 | } 30 | }, 31 | "4": { 32 | "inputs": { 33 | "ckpt_name": "SD1.5_photon_v1.safetensors" 34 | }, 35 | "class_type": "CheckpointLoaderSimple", 36 | "_meta": { 37 | "title": "加载检查点" 38 | } 39 | }, 40 | "5": { 41 | "inputs": { 42 | "width": 512, 43 | "height": 512, 44 | "batch_size": 64 45 | }, 46 | "class_type": "EmptyLatentImage", 47 | "_meta": { 48 | "title": "空潜空间图像" 49 | } 50 | }, 51 | "6": { 52 | "inputs": { 53 | "lora_name": "AnimateLCM_sd15_t2v_lora.safetensors", 54 | "strength_model": 0.8, 55 | "model": [ 56 | "4", 57 | 0 58 | ] 59 | }, 60 | "class_type": "LoraLoaderModelOnly", 61 | "_meta": { 62 | "title": "仅加载LoRA模型" 63 | } 64 | }, 65 | "7": { 66 | "inputs": { 67 | "sampling": "lcm", 68 | "zsnr": false, 69 | "model": [ 70 | "6", 71 | 0 72 | ] 73 | }, 74 | "class_type": "ModelSamplingDiscrete", 75 | "_meta": { 76 | "title": "模型采样离散" 77 | } 78 | }, 79 | "8": { 80 | "inputs": { 81 | "start_percent": 0, 82 | "end_percent": 1, 83 | "motion_model": [ 84 | "9", 85 | 0 86 | ] 87 | }, 88 | "class_type": "ADE_ApplyAnimateDiffModel", 89 | "_meta": { 90 | "title": "Apply AnimateDiff Model (Adv.) 🎭🅐🅓②" 91 | } 92 | }, 93 | "9": { 94 | "inputs": { 95 | "model_name": "AnimateLCM_sd15_t2v.ckpt" 96 | }, 97 | "class_type": "ADE_LoadAnimateDiffModel", 98 | "_meta": { 99 | "title": "Load AnimateDiff Model 🎭🅐🅓②" 100 | } 101 | }, 102 | "10": { 103 | "inputs": { 104 | "beta_schedule": "lcm avg(sqrt_linear,linear)", 105 | "model": [ 106 | "7", 107 | 0 108 | ], 109 | "m_models": [ 110 | "8", 111 | 0 112 | ], 113 | "context_options": [ 114 | "20", 115 | 0 116 | ] 117 | }, 118 | "class_type": "ADE_UseEvolvedSampling", 119 | "_meta": { 120 | "title": "Use Evolved Sampling 🎭🅐🅓②" 121 | } 122 | }, 123 | "11": { 124 | "inputs": { 125 | "seed": 33698133299972, 126 | "steps": 8, 127 | "cfg": 1.8, 128 | "sampler_name": "lcm", 129 | "scheduler": "sgm_uniform", 130 | "denoise": 1, 131 | "model": [ 132 | "10", 133 | 0 134 | ], 135 | "positive": [ 136 | "13", 137 | 0 138 | ], 139 | "negative": [ 140 | "1", 141 | 0 142 | ], 143 | "latent_image": [ 144 | "5", 145 | 0 146 | ] 147 | }, 148 | "class_type": "KSampler", 149 | "_meta": { 150 | "title": "K采样器" 151 | } 152 | }, 153 | "13": { 154 | "inputs": { 155 | "text": "jellyfish in the sea,realistic,8k,best quality", 156 | "clip": [ 157 | "4", 158 | 1 159 | ] 160 | }, 161 | "class_type": "CLIPTextEncode", 162 | "_meta": { 163 | "title": "CLIP文本编码器 正向提示词" 164 | } 165 | }, 166 | "20": { 167 | "inputs": { 168 | "context_length": 16, 169 | "context_stride": 1, 170 | "context_overlap": 4, 171 | "fuse_method": "pyramid", 172 | "use_on_equal_length": false, 173 | "start_percent": 0, 174 | "guarantee_steps": 1 175 | }, 176 | "class_type": "ADE_StandardUniformContextOptions", 177 | "_meta": { 178 | "title": "Context Options◆Standard Uniform 🎭🅐🅓" 179 | } 180 | }, 181 | "21": { 182 | "inputs": { 183 | "frame_rate": 8, 184 | "quality": 75, 185 | "broadcast": false, 186 | "images": [ 187 | "2", 188 | 0 189 | ] 190 | }, 191 | "class_type": "VideotoTD", 192 | "_meta": { 193 | "title": "VideotoTD" 194 | } 195 | } 196 | } 197 | -------------------------------------------------------------------------------- /Workflows/Animatediff_Image2Video_Scribble.json: -------------------------------------------------------------------------------- 1 | { 2 | "3": { 3 | "inputs": { 4 | "seed": 945442756343725, 5 | "steps": 8, 6 | "cfg": 2, 7 | "sampler_name": "lcm", 8 | "scheduler": "sgm_uniform", 9 | "denoise": 1, 10 | "model": [ 11 | "39", 12 | 0 13 | ], 14 | "positive": [ 15 | "48", 16 | 0 17 | ], 18 | "negative": [ 19 | "48", 20 | 1 21 | ], 22 | "latent_image": [ 23 | "5", 24 | 0 25 | ] 26 | }, 27 | "class_type": "KSampler", 28 | "_meta": { 29 | "title": "K采样器" 30 | } 31 | }, 32 | "4": { 33 | "inputs": { 34 | "ckpt_name": "SD1.5_photon_v1.safetensors" 35 | }, 36 | "class_type": "CheckpointLoaderSimple", 37 | "_meta": { 38 | "title": "加载检查点" 39 | } 40 | }, 41 | "5": { 42 | "inputs": { 43 | "width": 512, 44 | "height": 512, 45 | "batch_size": 32 46 | }, 47 | "class_type": "EmptyLatentImage", 48 | "_meta": { 49 | "title": "空潜空间图像" 50 | } 51 | }, 52 | "6": { 53 | "inputs": { 54 | "text": "jellyfish in the sea,ink,8k", 55 | "clip": [ 56 | "62", 57 | 1 58 | ] 59 | }, 60 | "class_type": "CLIPTextEncode", 61 | "_meta": { 62 | "title": "CLIP文本编码器 正向提示词" 63 | } 64 | }, 65 | "7": { 66 | "inputs": { 67 | "text": "low quality", 68 | "clip": [ 69 | "62", 70 | 1 71 | ] 72 | }, 73 | "class_type": "CLIPTextEncode", 74 | "_meta": { 75 | "title": "CLIP文本编码器 负向提示词" 76 | } 77 | }, 78 | "8": { 79 | "inputs": { 80 | "samples": [ 81 | "3", 82 | 0 83 | ], 84 | "vae": [ 85 | "63", 86 | 0 87 | ] 88 | }, 89 | "class_type": "VAEDecode", 90 | "_meta": { 91 | "title": "VAE解码" 92 | } 93 | }, 94 | "37": { 95 | "inputs": { 96 | "lora_name": "AnimateLCM_sd15_t2v_lora.safetensors", 97 | "strength_model": 0.8, 98 | "model": [ 99 | "4", 100 | 0 101 | ] 102 | }, 103 | "class_type": "LoraLoaderModelOnly", 104 | "_meta": { 105 | "title": "仅加载LoRA模型" 106 | } 107 | }, 108 | "38": { 109 | "inputs": { 110 | "sampling": "lcm", 111 | "zsnr": false, 112 | "model": [ 113 | "62", 114 | 0 115 | ] 116 | }, 117 | "class_type": "ModelSamplingDiscrete", 118 | "_meta": { 119 | "title": "模型采样离散" 120 | } 121 | }, 122 | "39": { 123 | "inputs": { 124 | "beta_schedule": "lcm avg(sqrt_linear,linear)", 125 | "model": [ 126 | "38", 127 | 0 128 | ], 129 | "m_models": [ 130 | "40", 131 | 0 132 | ], 133 | "context_options": [ 134 | "59", 135 | 0 136 | ] 137 | }, 138 | "class_type": "ADE_UseEvolvedSampling", 139 | "_meta": { 140 | "title": "Use Evolved Sampling 🎭🅐🅓②" 141 | } 142 | }, 143 | "40": { 144 | "inputs": { 145 | "start_percent": 0, 146 | "end_percent": 1, 147 | "motion_model": [ 148 | "41", 149 | 0 150 | ] 151 | }, 152 | "class_type": "ADE_ApplyAnimateDiffModel", 153 | "_meta": { 154 | "title": "Apply AnimateDiff Model (Adv.) 🎭🅐🅓②" 155 | } 156 | }, 157 | "41": { 158 | "inputs": { 159 | "model_name": "AnimateLCM_sd15_t2v.ckpt" 160 | }, 161 | "class_type": "ADE_LoadAnimateDiffModel", 162 | "_meta": { 163 | "title": "Load AnimateDiff Model 🎭🅐🅓②" 164 | } 165 | }, 166 | "48": { 167 | "inputs": { 168 | "strength": 0.9, 169 | "start_percent": 0, 170 | "end_percent": 1, 171 | "positive": [ 172 | "6", 173 | 0 174 | ], 175 | "negative": [ 176 | "7", 177 | 0 178 | ], 179 | "control_net": [ 180 | "49", 181 | 0 182 | ], 183 | "image": [ 184 | "54", 185 | 0 186 | ], 187 | "vae": [ 188 | "63", 189 | 0 190 | ] 191 | }, 192 | "class_type": "ControlNetApplyAdvanced", 193 | "_meta": { 194 | "title": "应用控制网" 195 | } 196 | }, 197 | "49": { 198 | "inputs": { 199 | "sparsectrl_name": "v3_sd15_sparsectrl_scribble.ckpt", 200 | "use_motion": true, 201 | "motion_strength": 1, 202 | "motion_scale": 1, 203 | "context_aware": "nearest_hint", 204 | "sparse_hint_mult": 1, 205 | "sparse_nonhint_mult": 1, 206 | "sparse_mask_mult": 1, 207 | "sparse_method": [ 208 | "50", 209 | 0 210 | ] 211 | }, 212 | "class_type": "ACN_SparseCtrlLoaderAdvanced", 213 | "_meta": { 214 | "title": "Load SparseCtrl Model 🛂🅐🅒🅝" 215 | } 216 | }, 217 | "50": { 218 | "inputs": { 219 | "spread": "uniform" 220 | }, 221 | "class_type": "ACN_SparseCtrlSpreadMethodNode", 222 | "_meta": { 223 | "title": "SparseCtrl Spread Method 🛂🅐🅒🅝" 224 | } 225 | }, 226 | "54": { 227 | "inputs": { 228 | "width": 512, 229 | "height": 512, 230 | "upscale_method": "nearest-exact", 231 | "keep_proportion": false, 232 | "divisible_by": 2, 233 | "crop": "disabled", 234 | "image": [ 235 | "65", 236 | 0 237 | ] 238 | }, 239 | "class_type": "ImageResizeKJ", 240 | "_meta": { 241 | "title": "Resize Image" 242 | } 243 | }, 244 | "59": { 245 | "inputs": { 246 | "context_length": 16, 247 | "context_stride": 1, 248 | "context_overlap": 4, 249 | "closed_loop": false, 250 | "fuse_method": "pyramid", 251 | "use_on_equal_length": false, 252 | "start_percent": 0, 253 | "guarantee_steps": 1 254 | }, 255 | "class_type": "ADE_LoopedUniformContextOptions", 256 | "_meta": { 257 | "title": "Context Options◆Looped Uniform 🎭🅐🅓" 258 | } 259 | }, 260 | "62": { 261 | "inputs": { 262 | "lora_name": "SD1.5_(水墨山水)Ink scenery.safetensors", 263 | "strength_model": 1, 264 | "strength_clip": 1, 265 | "model": [ 266 | "37", 267 | 0 268 | ], 269 | "clip": [ 270 | "4", 271 | 1 272 | ] 273 | }, 274 | "class_type": "LoraLoader", 275 | "_meta": { 276 | "title": "加载LoRA" 277 | } 278 | }, 279 | "63": { 280 | "inputs": { 281 | "vae_name": "vaeFtMse840000EmaPruned_vaeFtMse840k.safetensors" 282 | }, 283 | "class_type": "VAELoader", 284 | "_meta": { 285 | "title": "加载VAE" 286 | } 287 | }, 288 | "65": { 289 | "inputs": { 290 | "image": "" 291 | }, 292 | "class_type": "LoadTDImage", 293 | "_meta": { 294 | "title": "Scribble 线框图" 295 | } 296 | }, 297 | "66": { 298 | "inputs": { 299 | "frame_rate": 8, 300 | "quality": 75, 301 | "broadcast": false, 302 | "images": [ 303 | "8", 304 | 0 305 | ] 306 | }, 307 | "class_type": "VideotoTD", 308 | "_meta": { 309 | "title": "VideotoTD" 310 | } 311 | } 312 | } 313 | -------------------------------------------------------------------------------- /Workflows/Hunyuan3D_PointCloud.json: -------------------------------------------------------------------------------- 1 | { 2 | "10": { 3 | "inputs": { 4 | "model": "hunyuan3d-dit-v2-0-fp16.safetensors", 5 | "attention_mode": "sdpa", 6 | "cublas_ops": false 7 | }, 8 | "class_type": "Hy3DModelLoader", 9 | "_meta": { 10 | "title": "Hy3DModelLoader" 11 | } 12 | }, 13 | "28": { 14 | "inputs": { 15 | "model": "hunyuan3d-delight-v2-0" 16 | }, 17 | "class_type": "DownloadAndLoadHy3DDelightModel", 18 | "_meta": { 19 | "title": "(Down)Load Hy3D DelightModel" 20 | } 21 | }, 22 | "35": { 23 | "inputs": { 24 | "steps": 50, 25 | "width": 512, 26 | "height": 512, 27 | "cfg_image": 1, 28 | "seed": 0, 29 | "delight_pipe": [ 30 | "28", 31 | 0 32 | ], 33 | "image": [ 34 | "64", 35 | 0 36 | ], 37 | "scheduler": [ 38 | "148", 39 | 0 40 | ] 41 | }, 42 | "class_type": "Hy3DDelightImage", 43 | "_meta": { 44 | "title": "Hy3DDelightImage" 45 | } 46 | }, 47 | "52": { 48 | "inputs": { 49 | "width": 543, 50 | "height": 512, 51 | "interpolation": "lanczos", 52 | "method": "pad", 53 | "condition": "always", 54 | "multiple_of": 2, 55 | "image": [ 56 | "170", 57 | 0 58 | ] 59 | }, 60 | "class_type": "ImageResize+", 61 | "_meta": { 62 | "title": "🔧 Image Resize" 63 | } 64 | }, 65 | "56": { 66 | "inputs": { 67 | "rembg_session": [ 68 | "153", 69 | 0 70 | ], 71 | "image": [ 72 | "52", 73 | 0 74 | ] 75 | }, 76 | "class_type": "ImageRemoveBackground+", 77 | "_meta": { 78 | "title": "🔧 Image Remove Background" 79 | } 80 | }, 81 | "59": { 82 | "inputs": { 83 | "remove_floaters": true, 84 | "remove_degenerate_faces": true, 85 | "reduce_faces": true, 86 | "max_facenum": 50000, 87 | "smooth_normals": false, 88 | "trimesh": [ 89 | "140", 90 | 0 91 | ] 92 | }, 93 | "class_type": "Hy3DPostprocessMesh", 94 | "_meta": { 95 | "title": "Hy3D Postprocess Mesh" 96 | } 97 | }, 98 | "61": { 99 | "inputs": { 100 | "camera_azimuths": "0, 90, 180, 270, 0, 180", 101 | "camera_elevations": "0, 0, 0, 0, 90, -90", 102 | "view_weights": "1, 0.1, 0.5, 0.1, 0.05, 0.05", 103 | "camera_distance": 1.45, 104 | "ortho_scale": 1.2 105 | }, 106 | "class_type": "Hy3DCameraConfig", 107 | "_meta": { 108 | "title": "Hy3D Camera Config" 109 | } 110 | }, 111 | "64": { 112 | "inputs": { 113 | "x": 0, 114 | "y": 0, 115 | "resize_source": false, 116 | "destination": [ 117 | "133", 118 | 0 119 | ], 120 | "source": [ 121 | "52", 122 | 0 123 | ], 124 | "mask": [ 125 | "56", 126 | 1 127 | ] 128 | }, 129 | "class_type": "ImageCompositeMasked", 130 | "_meta": { 131 | "title": "合成图像（遮罩）" 132 | } 133 | }, 134 | "79": { 135 | "inputs": { 136 | "render_size": 1024, 137 | "texture_size": 2048, 138 | "normal_space": "world", 139 | "trimesh": [ 140 | "83", 141 | 0 142 | ], 143 | "camera_config": [ 144 | "61", 145 | 0 146 | ] 147 | }, 148 | "class_type": "Hy3DRenderMultiView", 149 | "_meta": { 150 | "title": "Hy3D Render MultiView" 151 | } 152 | }, 153 | "83": { 154 | "inputs": { 155 | "trimesh": [ 156 | "59", 157 | 0 158 | ] 159 | }, 160 | "class_type": "Hy3DMeshUVWrap", 161 | "_meta": { 162 | "title": "Hy3D Mesh UV Wrap" 163 | } 164 | }, 165 | "85": { 166 | "inputs": { 167 | "model": "hunyuan3d-paint-v2-0" 168 | }, 169 | "class_type": "DownloadAndLoadHy3DPaintModel", 170 | "_meta": { 171 | "title": "(Down)Load Hy3D PaintModel" 172 | } 173 | }, 174 | "88": { 175 | "inputs": { 176 | "view_size": 512, 177 | "steps": 25, 178 | "seed": 1024, 179 | "denoise_strength": 1, 180 | "pipeline": [ 181 | "85", 182 | 0 183 | ], 184 | "ref_image": [ 185 | "35", 186 | 0 187 | ], 188 | "normal_maps": [ 189 | "79", 190 | 0 191 | ], 192 | "position_maps": [ 193 | "79", 194 | 1 195 | ], 196 | "camera_config": [ 197 | "61", 198 | 0 199 | ], 200 | "scheduler": [ 201 | "149", 202 | 0 203 | ] 204 | }, 205 | "class_type": "Hy3DSampleMultiView", 206 | "_meta": { 207 | "title": "Hy3D Sample MultiView" 208 | } 209 | }, 210 | "92": { 211 | "inputs": { 212 | "images": [ 213 | "117", 214 | 0 215 | ], 216 | "renderer": [ 217 | "79", 218 | 2 219 | ], 220 | "camera_config": [ 221 | "61", 222 | 0 223 | ] 224 | }, 225 | "class_type": "Hy3DBakeFromMultiview", 226 | "_meta": { 227 | "title": "Hy3D Bake From Multiview" 228 | } 229 | }, 230 | "98": { 231 | "inputs": { 232 | "texture": [ 233 | "104", 234 | 0 235 | ], 236 | "renderer": [ 237 | "129", 238 | 2 239 | ] 240 | }, 241 | "class_type": "Hy3DApplyTexture", 242 | "_meta": { 243 | "title": "Hy3D Apply Texture" 244 | } 245 | }, 246 | "104": { 247 | "inputs": { 248 | "inpaint_radius": 3, 249 | "inpaint_method": "ns", 250 | "texture": [ 251 | "129", 252 | 0 253 | ], 254 | "mask": [ 255 | "129", 256 | 1 257 | ] 258 | }, 259 | "class_type": "CV2InpaintTexture", 260 | "_meta": { 261 | "title": "CV2 Inpaint Texture" 262 | } 263 | }, 264 | "117": { 265 | "inputs": { 266 | "width": 2048, 267 | "height": 2048, 268 | "interpolation": "lanczos", 269 | "method": "stretch", 270 | "condition": "always", 271 | "multiple_of": 0, 272 | "image": [ 273 | "88", 274 | 0 275 | ] 276 | }, 277 | "class_type": "ImageResize+", 278 | "_meta": { 279 | "title": "🔧 Image Resize" 280 | } 281 | }, 282 | "129": { 283 | "inputs": { 284 | "texture": [ 285 | "92", 286 | 0 287 | ], 288 | "mask": [ 289 | "92", 290 | 1 291 | ], 292 | "renderer": [ 293 | "92", 294 | 2 295 | ] 296 | }, 297 | "class_type": "Hy3DMeshVerticeInpaintTexture", 298 | "_meta": { 299 | "title": "Hy3D Mesh Vertice Inpaint Texture" 300 | } 301 | }, 302 | "132": { 303 | "inputs": { 304 | "value": 0.8, 305 | "width": 512, 306 | "height": 512 307 | }, 308 | "class_type": "SolidMask", 309 | "_meta": { 310 | "title": "纯块遮罩" 311 | } 312 | }, 313 | "133": { 314 | "inputs": { 315 | "mask": [ 316 | "132", 317 | 0 318 | ] 319 | }, 320 | "class_type": "MaskToImage", 321 | "_meta": { 322 | "title": "遮罩转换为图像" 323 | } 324 | }, 325 | "140": { 326 | "inputs": { 327 | "box_v": 1.01, 328 | "octree_resolution": 384, 329 | "num_chunks": 32000, 330 | "mc_level": 0, 331 | "mc_algo": "mc", 332 | "vae": [ 333 | "10", 334 | 1 335 | ], 336 | "latents": [ 337 | "141", 338 | 0 339 | ] 340 | }, 341 | "class_type": "Hy3DVAEDecode", 342 | "_meta": { 343 | "title": "Hy3D VAE Decode" 344 | } 345 | }, 346 | "141": { 347 | "inputs": { 348 | "guidance_scale": 5.5, 349 | "steps": 50, 350 | "seed": 126, 351 | "pipeline": [ 352 | "10", 353 | 0 354 | ], 355 | "image": [ 356 | "52", 357 | 0 358 | ], 359 | "mask": [ 360 | "56", 361 | 1 362 | ] 363 | }, 364 | "class_type": "Hy3DGenerateMesh", 365 | "_meta": { 366 | "title": "Hy3DGenerateMesh" 367 | } 368 | }, 369 | "148": { 370 | "inputs": { 371 | "scheduler": "Euler A", 372 | "sigmas": "default", 373 | "pipeline": [ 374 | "28", 375 | 0 376 | ] 377 | }, 378 | "class_type": "Hy3DDiffusersSchedulerConfig", 379 | "_meta": { 380 | "title": "Hy3D Diffusers Scheduler Config" 381 | } 382 | }, 383 | "149": { 384 | "inputs": { 385 | "scheduler": "Euler A", 386 | "sigmas": "default", 387 | "pipeline": [ 388 | "85", 389 | 0 390 | ] 391 | }, 392 | "class_type": "Hy3DDiffusersSchedulerConfig", 393 | "_meta": { 394 | "title": "Hy3D Diffusers Scheduler Config" 395 | } 396 | }, 397 | "153": { 398 | "inputs": { 399 | "mode": "fast", 400 | "use_jit": true 401 | }, 402 | "class_type": "TransparentBGSession+", 403 | "_meta": { 404 | "title": "🔧 InSPyReNet TransparentBG" 405 | } 406 | }, 407 | "169": { 408 | "inputs": { 409 | "broadcast": false, 410 | "trimesh": [ 411 | "98", 412 | 0 413 | ] 414 | }, 415 | "class_type": "Hy3DtoTD", 416 | "_meta": { 417 | "title": "Hy3DtoTD" 418 | } 419 | }, 420 | "170": { 421 | "inputs": { 422 | "image": "" 423 | }, 424 | "class_type": "LoadTDImage", 425 | "_meta": { 426 | "title": "LoadTDImage" 427 | } 428 | } 429 | } 430 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ComfyUI-TD 2 | 3 | This node implements seamless data interaction between [TouchDesigner](https://derivative.ca/) (hereinafter referred to as "TD") and [ComfyUI](https://github.com/comfyanonymous/ComfyUI). 4 | 5 | It supports real-time transmission of **Images**, **Videos**, **3D Models (Point Clouds)**, and **Audio** generated by ComfyUI into TD. 6 | 7 | | ![](Image/image.gif) | ![](Image/Video.gif) | 8 | | :---: | :---: | 9 | | ![](Image/PointCloud.gif) | ![](Image/audio.gif) | 10 | 11 | # [🇨🇳 中文 README](README_zh.md) 12 | 13 | ## User Notice 14 | 15 | - Some nodes in **ComfyUI-TD** are ported and optimized based on [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main). 16 | - **ComfyUI-TD** must be used in conjunction with the **ComfyUI2TD.tox** component (the plugin has been uploaded to the `tox` folder). 17 | - Please ensure the **ComfyUI2TD.tox** component is updated to **v_5.1.x** or higher. 18 | - This version features a complete code refactor, supporting video and 3D model (point cloud) data transmission. 19 | - The WebSocket interface has been rewritten to effectively resolve issues where data (images) might not return properly when using cloud-based ComfyUI under poor network conditions. 20 | - Starting from **v_5.1.x**, the **ComfyUI2TD.tox** component's preset workflows use ComfyUI-TD nodes and no longer rely on [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main). 21 | - The legacy **ComfyUI2TD.tox** component was developed based on the [TDComfyUI](https://github.com/olegchomp/TDComfyUI) project. Thanks to olegchomp! 22 | - If you need to use cloud-based ComfyUI, you can choose [Xiangongyun](https://www.xiangongyun.com/register/YALSMH) servers; the [matching image](https://www.xiangongyun.com/image/detail/5f185465-ef11-42e5-ba21-3ee07acb5403) is ready. 23 | 24 | --- 25 | 26 | ## Instructions 27 | 28 | ### Video Tutorials 29 | 30 | Please watch the following videos in order (Videos are in Chinese): 31 | 1. [ComfyUI2TD_v 5.1 New Version Tutorial *Must Watch* (Bilibili)](https://www.bilibili.com/video/BV1BqQ8YDEJq/?share_source=copy_web&vd_source=3900738a289821efe0ce52f9c9fb663f) 32 | 2. [ComfyUI2TD Basic Tutorial (Bilibili)](https://www.bilibili.com/video/BV18t4oeNEgj/?share_source=copy_web&vd_source=3900738a289821efe0ce52f9c9fb663f) 33 | 3. [Cloud Deployment ComfyUI Xiangongyun Tutorial (Bilibili)](https://www.bilibili.com/video/BV1RxUyYyEeU/?share_source=copy_web&vd_source=3900738a289821efe0ce52f9c9fb663f) 34 | 35 | ### Installation 36 | 37 | #### Method 1: Manager Installation 38 | Use [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager?tab=readme-ov-file) to search for **ComfyUI-TD** and install it directly. 39 | 40 | ![Manager nodes](Image/Manager.png) 41 | 42 | #### Method 2: Manual Installation 43 | Download and unzip this project into `X:\ComfyUI_windows_portable\ComfyUI\custom_nodes`. 44 | 45 | #### Method 3: Git Clone 46 | Install using the git command: 47 | ```bash 48 | cd custom_nodes 49 | git clone https://github.com/JiSenHua/ComfyUI-TD.git 50 | ``` 51 | 52 | #### Method 4: Injection Installation 53 | In conjunction with the **ComfyUI2TD.tox** component, use the `InjectFile` function to automatically inject nodes into `X:\ComfyUI_windows_portable\ComfyUI\custom_nodes`. 54 | 55 | --- 56 | 57 | ## Node Descriptions 58 | 59 | | | 60 | | :--- | 61 | | **Hy3DtoTD** | 62 | | - This node supports converting GLB models generated by **Hunyuan3D_V2** into point cloud data and returning it to **TD** for parsing, generating the corresponding CHOP component.
- Requires [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper) node.
- If you encounter difficulties installing, consider using the cloud [**Xiangongyun** image](https://www.xiangongyun.com/image/detail/5f185465-ef11-42e5-ba21-3ee07acb5403).
- The **ComfyUI2TD.tox** preset workflow **Hunyuan3DV2_PointCloud** provides a basic usage example for this node; the corresponding `.js` workflow file is in the `workflow` folder.
- The latest [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper) has changed all model workflows to `trimesh`.
- It is recommended to install [ComfyUI_essentials](https://github.com/cubiq/ComfyUI_essentials) to avoid errors when running preset workflows.
- `broadcast` parameter (default off): When enabled, generated point cloud data will be broadcast to all connected WebSocket clients. | 63 | 64 | | | 65 | | :--- | 66 | | **Tripo3DtoTD** | 67 | | - This node supports converting GLB models generated by **Tripo3D** into point cloud data and returning it to **TD** for parsing, generating the corresponding CHOP component.
- Requires [ComfyUI-Tripo](https://github.com/VAST-AI-Research/ComfyUI-Tripo) node.
- **API Update**: Now supports calling Tripo services directly via the official ComfyUI interface. You **NO LONGER** need to register on the Tripo website or apply for an API Key separately. The legacy API configuration method is **deprecated**.
- `broadcast` parameter (default off): When enabled, generated point cloud data will be broadcast to all connected WebSocket clients. | 68 | 69 | | | 70 | | :--- | 71 | | **TripoSRtoTD** | 72 | | - This node supports converting GLB models generated by **TripoSR** into point cloud data and returning it to **TD** for parsing, generating the corresponding CHOP component.
- Requires [ComfyUI-Flowty-TripoSR](https://github.com/flowtyone/ComfyUI-Flowty-TripoSR) node.
- `broadcast` parameter (default off): When enabled, generated point cloud data will be broadcast to all connected WebSocket clients. | 73 | 74 | | | 75 | | :--- | 76 | | **Comfy3DPacktoTD** | 77 | | - This node supports converting GLB models generated by **3DPack** into point cloud data and returning it to **TD** for parsing, generating the corresponding CHOP component.
- Requires [ComfyUI-3D-Pack](https://github.com/MrForExample/ComfyUI-3D-Pack) node.
- If you encounter difficulties installing, consider using the cloud [**Xiangongyun** image](https://www.xiangongyun.com/image/detail/5f185465-ef11-42e5-ba21-3ee07acb5403).
- The **ComfyUI2TD.tox** preset workflow **3DPack_xxx_PointCloud** provides a basic usage example for this node; the corresponding `.js` workflow file is in the `workflow` folder.
- **Hunyuan3D_V2** in **3DPack** and **Hunyuan3DWrapper** are not interchangeable; please ensure you use the corresponding transmission node.
- `broadcast` parameter (default off): When enabled, generated point cloud data will be broadcast to all connected WebSocket clients.
- **Note**: Currently, the Xiangongyun cloud image has only been tested with **TRELLIS**, **Hunyuan3D_V2**, **TripoSR**, and **StableFast3D**. Other 3D models have not been verified. Please report any issues in the Issues section. | 78 | 79 | | | 80 | | :--- | 81 | | **VideotoTD** | 82 | | - This node supports converting video to data and returning it to **TD** for parsing.
- Replace the **Video Combine 🎥🅥🅗🅢** node from [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite) in your workflow with this node.
- **frame_rate**: Affects the synthesis frame rate of the video; 8 is recommended.
- **quality**: Controls video compression quality; 75 is recommended for a good balance between data size and image quality.
- Data will be parsed by **ComfyUI2TD.tox**, and an `.MP4` file will be generated and saved in the `VideoOutput` folder in the local root directory.
- `broadcast` parameter (default off): When enabled, generated video data will be broadcast to all connected WebSocket clients. | 83 | 84 | | | 85 | | :--- | 86 | | **ImagetoTD** | 87 | | - Secondary development based on [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main) **Send Image (WebSocket)** node.
- This node supports returning images generated by ComfyUI to **TD** for parsing, generating the corresponding TOP component.
- `broadcast` parameter (default off): When enabled, generated image data will be broadcast to all connected WebSocket clients.
- Starting from **v_5.1.x**, **ComfyUI2TD.tox** preset workflows use ComfyUI-TD nodes instead of [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main). | 88 | 89 | | | 90 | | :--- | 91 | | **AudiotoTD** | 92 | | - This node supports returning audio generated by ComfyUI to **TD** for parsing, generating the corresponding CHOP component.
- **format**: Supports selecting output format as **wav** or **mp3**.
- **bitrate_kbps**: Sets the audio bitrate to adjust output audio quality and size.
- `broadcast` parameter (default off): When enabled, generated **audio data** will be broadcast to all connected WebSocket clients. | 93 | 94 | | | 95 | | :--- | 96 | | **ImagetoTD(JPEG)** | 97 | | - **JPEG Transmission Mode**: This node encodes the image generated by ComfyUI into **JPEG compressed format** and returns it to **TD** via WebSocket for parsing into a TOP component.
- **Performance & Scenario**: Compared to the standard `ImageToTD` method, JPEG format significantly reduces transmitted data size. This speeds up transmission and parsing, making it especially suitable for **Cloud ComfyUI** users to reduce bandwidth requirements.
- **Quality**: Due to compression, image quality will be lower than `ImageToTD`. Please balance according to actual needs.
- **quality**: Controls image compression quality; 85 is recommended.
- `broadcast` parameter (default off): When enabled, generated image data will be broadcast to all connected WebSocket clients. | 98 | 99 | | | 100 | | :--- | 101 | | **LoadTDImage** | 102 | | - Secondary development based on [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main) **Load Image (Base64)** node.
- This node supports using TOP components sent from TD as image input sources for ComfyUI.
- Starting from **v_5.1.x**, **ComfyUI2TD.tox** preset workflows use ComfyUI-TD nodes instead of [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main). | 103 | -------------------------------------------------------------------------------- /README_zh.md: -------------------------------------------------------------------------------- 1 | # ComfyUI-TD 2 | 3 | 本节点实现了 [TouchDesigner](https://derivative.ca/)（以下简称 "TD"）与 [ComfyUI](https://github.com/comfyanonymous/ComfyUI) 之间的无缝数据交互。 4 | 5 | 支持将 ComfyUI 生成的 **图像**、**视频**、**3D模型(点云)**、**音频** 数据实时传输进 TD。 6 | 7 | | ![](Image/image.gif) | ![](Image/Video.gif) | 8 | |:-------------------------:|:--------------------:| 9 | | ![](Image/PointCloud.gif) | ![](Image/audio.gif) | 10 | 11 | # [🇺🇸 English README](README.md) 12 | 13 | ## 用户须知 14 | 15 | - **ComfyUI-TD** 的部分节点基于 [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main) 进行了移植和优化。 16 | - **ComfyUI-TD** 需与 **ComfyUI2TD.tox** 组件配合使用（插件已上传至 `tox` 文件夹）。 17 | - 请确保 **ComfyUI2TD.tox** 组件版本更新至 **v_5.1.x** 或更高版本。 18 | - 此版本对组件代码进行了全面重构，支持了视频与 3D 模型(点云)的数据传输。 19 | - 重写了 WebSocket 接口，有效解决了在网络条件较差时，使用云端 ComfyUI 可能出现的数据（图像）无法正常返回的问题。 20 | - **ComfyUI2TD.tox** 组件至 **v_5.1.x** 版本起，预置的工作流将使用 ComfyUI-TD 节点，不再使用 [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main)。 21 | - 旧版 **ComfyUI2TD.tox** 组件基于 [TDComfyUI](https://github.com/olegchomp/TDComfyUI) 项目开发，感谢 olegchomp！ 22 | - 若需使用云端 ComfyUI，可选择 [仙宫云](https://www.xiangongyun.com/register/YALSMH) 服务器，[配套镜像](https://www.xiangongyun.com/image/detail/5f185465-ef11-42e5-ba21-3ee07acb5403) 已准备完毕。 23 | 24 | --- 25 | 26 | ## 使用说明 27 | 28 | ### 视频教程 29 | 30 | 请按照顺序观看以下视频： 31 | 32 | 1. [ComfyUI2TD_v 5.1 新版使用教程 *必看* (哔哩哔哩)](https://www.bilibili.com/video/BV1BqQ8YDEJq/?share_source=copy_web&vd_source=3900738a289821efe0ce52f9c9fb663f) 33 | 2. [ComfyUI2TD 基础使用教程 (哔哩哔哩)](https://www.bilibili.com/video/BV18t4oeNEgj/?share_source=copy_web&vd_source=3900738a289821efe0ce52f9c9fb663f) 34 | 3. [云端部署 ComfyUI 仙宫云使用教程 (哔哩哔哩)](https://www.bilibili.com/video/BV1RxUyYyEeU/?share_source=copy_web&vd_source=3900738a289821efe0ce52f9c9fb663f) 35 | 36 | ### ComfyUI-TD 节点安装 37 | 38 | #### 方法一：使用 Manager 安装 39 | 40 | 使用 [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager?tab=readme-ov-file) 搜索 **ComfyUI-TD**，并直接进行节点安装。 41 | 42 | ![Manager nodes](Image/Manager.png) 43 | 44 | #### 方法二：手动安装 45 | 46 | 将本项目下载后解压，放置于 `X:\ComfyUI_windows_portable\ComfyUI\custom_nodes`。 47 | 48 | #### 方法三：Git Clone 安装 49 | 50 | 使用 git 命令进行安装： 51 | 52 | ```bash 53 | cd custom_nodes 54 | git clone https://github.com/JiSenHua/ComfyUI-TD.git 55 | ``` 56 | 57 | #### 方法四：注入安装 58 | 59 | 配合 **ComfyUI2TD.tox** 组件，使用 `InjectFile 注入插件` 功能，将节点自动注入至 `X:\ComfyUI_windows_portable\ComfyUI\custom_nodes`。 60 | 61 | --- 62 | 63 | ## ComfyUI-TD 节点说明 64 | 65 | | | 66 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 67 | | **Hy3DtoTD** | 68 | | - 本节点支持将 **Hunyuan3D_V2 混元V2** 生成的 GLB 模型转换为点云数据，并返回至 **TD** 进行解析，从而生成对应的 CHOP 组件。
- 使用本节点需安装 [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper) 节点。
- 若安装遇到困难，可选择使用云端 [**仙宫云**镜像](https://www.xiangongyun.com/image/detail/5f185465-ef11-42e5-ba21-3ee07acb5403)。
- **ComfyUI2TD.tox** 预置的工作流 **Hunyuan3DV2_PointCloud** 提供了此节点的基础用法示例，对应的 `.js` 工作流文件已上传至 `workflow` 文件夹。
- 最新的 [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper) 已将所有模型工作流改为 `trimesh`。
- 建议配套安装 [ComfyUI_essentials](https://github.com/cubiq/ComfyUI_essentials)，避免运行预置工作流时报错。
- `broadcast` 广播参数（默认关闭）：启用该参数后，生成的点云数据将广播至所有已建立 WebSocket 连接的客户端。 | 69 | 70 | | | 71 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 72 | | **Tripo3DtoTD** | 73 | | - 本节点支持将 **Tripo3D** 生成的 GLB 模型转换为点云数据，并返回至 **TD** 进行解析，从而生成对应的 CHOP 组件。
- 使用本节点需安装 [ComfyUI-Tripo](https://github.com/VAST-AI-Research/ComfyUI-Tripo) 节点。
- **API 调用更新**：现已支持直接通过 ComfyUI 官方接口调用 Tripo 服务，**无需**再前往 Tripo 官网注册账户或单独申请 API Key，旧版的独立 API 配置方式已**弃用**。
- `broadcast` 广播参数（默认关闭）：启用该参数后，生成的点云数据将广播至所有已建立 WebSocket 连接的客户端。 | 74 | 75 | | | 76 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 77 | | **TripoSRtoTD** | 78 | | - 本节点支持将 **TripoSR** 生成的 GLB 模型转换为点云数据，并返回至 **TD** 进行解析，从而生成对应的 CHOP 组件。
- 使用本节点需安装 [ComfyUI-Flowty-TripoSR](https://github.com/flowtyone/ComfyUI-Flowty-TripoSR) 节点。
- `broadcast` 广播参数（默认关闭）：启用该参数后，生成的点云数据将广播至所有已建立 WebSocket 连接的客户端。 | 79 | 80 | | | 81 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 82 | | **Comfy3DPacktoTD** | 83 | | - 本节点支持将 **3DPack** 生成的 GLB 模型转换为点云数据，并返回至 **TD** 进行解析，从而生成对应的 CHOP 组件。
- 使用本节点需安装 [ComfyUI-3D-Pack](https://github.com/MrForExample/ComfyUI-3D-Pack) 节点。
- 若安装遇到困难，可选择使用云端 [**仙宫云**镜像](https://www.xiangongyun.com/image/detail/5f185465-ef11-42e5-ba21-3ee07acb5403)。
- **ComfyUI2TD.tox** 预置的工作流 **3DPack_xxx_PointCloud** 提供了此节点的基础用法示例，对应的 `.js` 工作流文件已上传至 `workflow` 文件夹。
- **3DPack** 中的 **Hunyuan3D_V2** 与 **Hunyuan3DWrapper** 并不互通，请确保使用各自对应的传输节点。
- `broadcast` 广播参数（默认关闭）：启用该参数后，生成的点云数据将广播至所有已建立 WebSocket 连接的客户端。
- **注意**：目前仙宫云端镜像仅对 **TRELLIS**、**Hunyuan3D_V2**、**TripoSR** 和 **StableFast3D** 进行了测试。其他 3D 模型尚未验证，如遇问题请在 Issues 中反馈。 | 84 | 85 | | | 86 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 87 | | **VideotoTD** | 88 | | - 本节点支持将视频转为数据，并返回至 **TD** 进行解析。
- 将你工作流中 [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite) 的 **Video Combine 🎥🅥🅗🅢** 替换成此节点。
- **frame_rate** 参数：影响视频的合成帧率，建议选择 8。
- **quality** 参数：控制视频的压缩质量，建议选择 75，以获得较好的数据大小与图像质量的平衡。
- 数据将通过 **ComfyUI2TD.tox** 解析，并生成 `.MP4` 格式文件保存在本地根目录中 `VideoOutput` 文件夹内。
- `broadcast` 广播参数（默认关闭）：启用该参数后，生成的视频数据将广播至所有已建立 WebSocket 连接的客户端。 | 89 | 90 | | | 91 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | 92 | | **ImagetoTD** | 93 | | - 基于 [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main) **Send Image (WebSocket)** 节点二次开发。
- 本节点支持将 ComfyUI 生成的图片返回至 **TD** 进行解析，从而生成对应的 TOP 组件。
- `broadcast` 广播参数（默认关闭）：启用该参数后，生成的图片数据将广播至所有已建立 WebSocket 连接的客户端。
- **ComfyUI2TD.tox** 组件至 **v_5.1.x** 版本起，预置的工作流将使用 ComfyUI-TD 节点，不再使用 [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main)。 | 94 | 95 | | | 96 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 97 | | **AudiotoTD** | 98 | | - 本节点支持将 ComfyUI 生成的音频返回至 **TD** 进行解析，从而生成对应的 CHOP 组件。
- **format** 参数：支持选择输出格式为 **wav** 或 **mp3**。
- **bitrate_kbps** 参数：设置音频比特率，用于调节输出音频的质量与大小。
- `broadcast` 广播参数（默认关闭）：启用该参数后，生成的 **音频数据** 将广播至所有已建立 WebSocket 连接的客户端。 | 99 | 100 | | | 101 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 102 | | **ImagetoTD(JPEG)** | 103 | | - **JPEG 传输模式**：本节点会将 ComfyUI 生成的图像编码为 **JPEG 压缩格式** 后，通过 WebSocket 返回至 **TD** 进行解析生成 TOP 元件。
- **性能与场景**：相比于标准的 `ImageToTD` 方式，JPEG 格式显著减少了传输的数据量。这不仅加快了传输与解析速度，更特别适合 **云端部署 ComfyUI** 的用户，能有效降低网络带宽需求。
- **画质说明**：由于采用了压缩传输，图像质量相比 `ImageToTD` 会有所降低，请根据实际需求权衡使用。
- **quality** 参数：控制图像的压缩质量，建议选择 85，以获得较好的数据大小与图像质量的平衡。
- `broadcast` 广播参数（默认关闭）：启用该参数后，生成的图片数据将广播至所有已建立 WebSocket 连接的客户端。 | 104 | 105 | | | 106 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 107 | | **LoadTDImage** | 108 | | - 基于 [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main) **Load Image (Base64)** 节点二次开发。
- 本节点支持由 TD 发送的 TOP 元件作为图像输入源发送给 ComfyUI。
- **ComfyUI2TD.tox** 组件至 **v_5.1.x** 版本起，预置的工作流将使用 ComfyUI-TD 节点，不再使用 [ComfyUI-Tooling-Nodes](https://github.com/Acly/comfyui-tooling-nodes/tree/main)。 | 109 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /node.py: -------------------------------------------------------------------------------- 1 | import struct 2 | import numpy as np 3 | import io 4 | from server import PromptServer, BinaryEventTypes 5 | from pathlib import Path 6 | from PIL import Image 7 | import base64 8 | from io import BytesIO 9 | import torch 10 | import tempfile 11 | import os 12 | 13 | 14 | class Hy3DtoTD: 15 | #https://github.com/kijai/ComfyUI-Hunyuan3DWrapper 16 | @classmethod 17 | def INPUT_TYPES(s): 18 | return { 19 | "required": { 20 | "trimesh": ("TRIMESH",), 21 | "broadcast": ("BOOLEAN", {"default": False}), 22 | } 23 | } 24 | 25 | RETURN_TYPES = () 26 | FUNCTION = "send_mesh" 27 | OUTPUT_NODE = True 28 | CATEGORY = "TouchDesigner" 29 | 30 | def send_mesh(self, trimesh, broadcast): 31 | try: 32 | processed_mesh = self.process_mesh(trimesh) 33 | return self._send_to_td(processed_mesh, broadcast) 34 | except Exception as e: 35 | raise ValueError(f"Error processing mesh: {str(e)}") 36 | 37 | def process_mesh(self, mesh): 38 | try: 39 | import trimesh 40 | 41 | if isinstance(mesh, trimesh.Scene): 42 | meshes = [] 43 | for m in mesh.geometry.values(): 44 | if isinstance(m, trimesh.Trimesh): 45 | meshes.append(m) 46 | if meshes: 47 | mesh = trimesh.util.concatenate(meshes) 48 | else: 49 | raise ValueError("No valid mesh data found") 50 | 51 | if not isinstance(mesh, trimesh.Trimesh): 52 | raise ValueError("Input data is not a valid Trimesh object") 53 | 54 | mesh.fix_normals() 55 | 56 | if hasattr(mesh.visual, 'vertex_colors') and mesh.visual.vertex_colors is not None: 57 | pass 58 | elif hasattr(mesh.visual, 'uv') and mesh.visual.uv is not None: 59 | mesh.visual = mesh.visual.to_color() 60 | else: 61 | mesh.visual = trimesh.visual.ColorVisuals(mesh) 62 | mesh.visual.vertex_colors = np.tile([200, 200, 200, 255], (len(mesh.vertices), 1)) 63 | 64 | return mesh 65 | 66 | except ImportError: 67 | raise ImportError("Please install the trimesh library: pip install trimesh") 68 | except Exception as e: 69 | raise ValueError(f"Error processing mesh: {str(e)}") 70 | 71 | def _send_to_td(self, mesh, broadcast): 72 | try: 73 | import trimesh 74 | 75 | buffer = io.BytesIO() 76 | mesh.export(file_obj=buffer, file_type='ply') 77 | binary_data = buffer.getvalue() 78 | buffer.close() 79 | 80 | server = PromptServer.instance 81 | sid = None if broadcast else server.client_id 82 | server.send_sync(1000, binary_data, sid=sid) 83 | 84 | return {"ui": { 85 | "mesh": [{ 86 | "source": "websocket", 87 | "content-type": "model/ply", 88 | "type": "output", 89 | }] 90 | }} 91 | except ImportError: 92 | raise ImportError("Please install the trimesh library: pip install trimesh") 93 | except Exception as e: 94 | raise ValueError(f"Error sending mesh to TouchDesigner: {str(e)}") 95 | 96 | 97 | class Tripo3DtoTD: 98 | #https://github.com/VAST-AI-Research/ComfyUI-Tripo 99 | @classmethod 100 | def INPUT_TYPES(s): 101 | return { 102 | "required": { 103 | "model_file": ("STRING",), 104 | "broadcast": ("BOOLEAN", {"default": False}), 105 | } 106 | } 107 | 108 | RETURN_TYPES = () 109 | FUNCTION = "send_mesh" 110 | OUTPUT_NODE = True 111 | CATEGORY = "TouchDesigner" 112 | 113 | def send_mesh(self, model_file, broadcast): 114 | try: 115 | processed_mesh = self.process_mesh(model_file) 116 | return self._send_to_td(processed_mesh, broadcast) 117 | except Exception as e: 118 | raise ValueError(f"Error processing mesh: {str(e)}") 119 | 120 | def process_mesh(self, model_file): 121 | try: 122 | import trimesh 123 | if not model_file.startswith(('http://', 'https://')) and not os.path.isabs(model_file): 124 | try: 125 | import folder_paths 126 | output_dir = folder_paths.get_output_directory() 127 | local_path = os.path.join(output_dir, model_file) 128 | if os.path.exists(local_path): 129 | model_file = local_path 130 | except: 131 | pass 132 | 133 | mesh = trimesh.load(model_file) 134 | 135 | if isinstance(mesh, trimesh.Scene): 136 | meshes = [] 137 | for m in mesh.geometry.values(): 138 | if isinstance(m, trimesh.Trimesh): 139 | self._process_vertex_colors(m) 140 | meshes.append(m) 141 | 142 | if meshes: 143 | mesh = trimesh.util.concatenate(meshes) 144 | else: 145 | raise ValueError("No valid mesh data found") 146 | 147 | if not isinstance(mesh, trimesh.Trimesh): 148 | raise ValueError("Input data is not a valid Trimesh object") 149 | 150 | mesh.fix_normals() 151 | 152 | if not hasattr(mesh.visual, 'vertex_colors') or mesh.visual.vertex_colors is None: 153 | mesh.visual = trimesh.visual.ColorVisuals(mesh) 154 | mesh.visual.vertex_colors = np.tile([200, 200, 200, 255], (len(mesh.vertices), 1)) 155 | else: 156 | if mesh.visual.vertex_colors.max() <= 1.0: 157 | mesh.visual.vertex_colors = (mesh.visual.vertex_colors * 255).astype(np.uint8) 158 | else: 159 | mesh.visual.vertex_colors = mesh.visual.vertex_colors.astype(np.uint8) 160 | 161 | return mesh 162 | 163 | except ImportError: 164 | raise ImportError("Please install the trimesh library: pip install trimesh") 165 | except Exception as e: 166 | raise ValueError(f"Error processing mesh: {str(e)}") 167 | 168 | def _process_vertex_colors(self, m): 169 | try: 170 | import trimesh 171 | 172 | if not hasattr(m.visual, 'vertex_colors') or m.visual.vertex_colors is None: 173 | if hasattr(m.visual, 'material'): 174 | material = m.visual.material 175 | if hasattr(material, 'baseColorTexture') and material.baseColorTexture is not None: 176 | if hasattr(m.visual, 'uv') and m.visual.uv is not None: 177 | self._extract_colors_from_texture(m) 178 | elif hasattr(material, 'baseColorFactor'): 179 | color = np.array(material.baseColorFactor) 180 | if len(color) == 3: 181 | color = np.append(color, 1.0) 182 | color = np.clip(color, 0, 1) 183 | color = (color * 255).astype(np.uint8) 184 | m.visual = trimesh.visual.ColorVisuals(m) 185 | m.visual.vertex_colors = np.tile(color, (len(m.vertices), 1)) 186 | else: 187 | m.visual = trimesh.visual.ColorVisuals(m) 188 | m.visual.vertex_colors = np.tile([200, 200, 200, 255], (len(m.vertices), 1)) 189 | else: 190 | m.visual = trimesh.visual.ColorVisuals(m) 191 | m.visual.vertex_colors = np.tile([200, 200, 200, 255], (len(m.vertices), 1)) 192 | else: 193 | if m.visual.vertex_colors.max() <= 1.0: 194 | m.visual.vertex_colors = (m.visual.vertex_colors * 255).astype(np.uint8) 195 | else: 196 | m.visual.vertex_colors = m.visual.vertex_colors.astype(np.uint8) 197 | except ImportError: 198 | raise ImportError("Please install the trimesh library: pip install trimesh") 199 | except Exception as e: 200 | raise ValueError(f"Error processing vertex colors: {str(e)}") 201 | 202 | def _extract_colors_from_texture(self, m): 203 | try: 204 | import trimesh 205 | 206 | material = m.visual.material 207 | image = material.baseColorTexture 208 | if image is not None: 209 | image_data = np.array(image) 210 | if image_data.dtype != np.uint8: 211 | image_data = (np.clip(image_data, 0, 1) * 255).astype(np.uint8) 212 | 213 | uvs = m.visual.uv 214 | uvs = np.clip(uvs, 0, 1) 215 | h, w = image_data.shape[0], image_data.shape[1] 216 | i = ((1 - uvs[:, 1]) * (h - 1)).astype(int) 217 | j = (uvs[:, 0] * (w - 1)).astype(int) 218 | i = np.clip(i, 0, h - 1) 219 | j = np.clip(j, 0, w - 1) 220 | colors = image_data[i, j, :4] 221 | m.visual = trimesh.visual.ColorVisuals(m) 222 | m.visual.vertex_colors = colors 223 | else: 224 | m.visual = trimesh.visual.ColorVisuals(m) 225 | m.visual.vertex_colors = np.tile([200, 200, 200, 255], (len(m.vertices), 1)) 226 | except ImportError: 227 | raise ImportError("Please install the trimesh library: pip install trimesh") 228 | except Exception as e: 229 | raise ValueError(f"Error extracting colors from texture: {str(e)}") 230 | 231 | def _send_to_td(self, mesh, broadcast): 232 | try: 233 | if not hasattr(mesh.visual, 'vertex_colors') or mesh.visual.vertex_colors is None: 234 | colors = np.tile([200, 200, 200, 255], (len(mesh.vertices), 1)).astype(np.uint8) 235 | else: 236 | colors = mesh.visual.vertex_colors.astype(np.uint8) 237 | 238 | header = [ 239 | "ply", 240 | "format binary_little_endian 1.0", 241 | f"element vertex {len(mesh.vertices)}", 242 | "property float x", 243 | "property float y", 244 | "property float z", 245 | "property uchar red", 246 | "property uchar green", 247 | "property uchar blue", 248 | "property uchar alpha", 249 | "end_header\n" 250 | ] 251 | header = '\n'.join(header).encode('ascii') 252 | 253 | vertices = mesh.vertices.astype('I", type_num) 352 | bytesIO.write(header) 353 | 354 | image.save(bytesIO, format="JPEG", quality=quality) 355 | jpeg_bytes = bytesIO.getvalue() 356 | 357 | server = PromptServer.instance 358 | sid = None if broadcast else server.client_id 359 | 360 | server.send_sync( 361 | BinaryEventTypes.PREVIEW_IMAGE, 362 | jpeg_bytes, 363 | sid, 364 | ) 365 | results.append({ 366 | "source": "websocket", 367 | "content-type": "image/jpeg", 368 | "type": "output", 369 | }) 370 | 371 | return {"ui": {"images": results}} 372 | 373 | class Comfy3DPacktoTD: 374 | #https://github.com/MrForExample/ComfyUI-3D-Pack 375 | @classmethod 376 | def INPUT_TYPES(cls): 377 | return { 378 | "required": { 379 | "mesh": ("MESH",), 380 | "broadcast": ("BOOLEAN", {"default": False}), 381 | }, 382 | } 383 | 384 | RETURN_TYPES = () 385 | FUNCTION = "send_to_td" 386 | OUTPUT_NODE = True 387 | CATEGORY = "TouchDesigner" 388 | 389 | def send_to_td(self, mesh, broadcast): 390 | try: 391 | vertices = mesh.v.cpu().numpy().astype(np.float32) 392 | colors = None 393 | color_source = "none" 394 | 395 | if hasattr(mesh, 'albedo') and mesh.albedo is not None: 396 | albedo = mesh.albedo 397 | if hasattr(albedo, 'cpu'): 398 | albedo = albedo.cpu() 399 | if hasattr(albedo, 'numpy'): 400 | albedo = albedo.numpy() 401 | 402 | if len(albedo.shape) > 1: 403 | if hasattr(mesh, 'vt') and mesh.vt is not None: 404 | uvs = mesh.vt.cpu().numpy() 405 | uvs = np.clip(uvs, 0, 1) 406 | tex_h, tex_w = albedo.shape[:2] 407 | 408 | mapped_u, mapped_v = uvs[:, 0], uvs[:, 1] 409 | 410 | tex_x = mapped_u * (tex_w - 1) 411 | tex_y = mapped_v * (tex_h - 1) 412 | 413 | tex_x0 = np.floor(tex_x).astype(np.int32) 414 | tex_y0 = np.floor(tex_y).astype(np.int32) 415 | tex_x1 = np.minimum(tex_x0 + 1, tex_w - 1) 416 | tex_y1 = np.minimum(tex_y0 + 1, tex_h - 1) 417 | 418 | wx = tex_x - tex_x0 419 | wy = tex_y - tex_y0 420 | 421 | c00 = albedo[tex_y0, tex_x0] 422 | c01 = albedo[tex_y0, tex_x1] 423 | c10 = albedo[tex_y1, tex_x0] 424 | c11 = albedo[tex_y1, tex_x1] 425 | 426 | c0 = c00 * (1 - wx[:, np.newaxis]) + c01 * wx[:, np.newaxis] 427 | c1 = c10 * (1 - wx[:, np.newaxis]) + c11 * wx[:, np.newaxis] 428 | colors = c0 * (1 - wy[:, np.newaxis]) + c1 * wy[:, np.newaxis] 429 | 430 | color_source = "albedo_with_uv_no_v_flip" 431 | else: 432 | if albedo.shape[0] == len(vertices): 433 | colors = albedo 434 | color_source = "albedo_direct" 435 | else: 436 | colors = albedo 437 | color_source = "albedo_scalar" 438 | 439 | if colors is None and hasattr(mesh, 'vc') and mesh.vc is not None: 440 | colors = mesh.vc.cpu().numpy() 441 | color_source = "vertex_colors" 442 | 443 | if colors is None: 444 | positions = vertices - vertices.min(axis=0) 445 | positions = positions / positions.max(axis=0) if positions.max() > 0 else positions 446 | colors = positions 447 | color_source = "generated" 448 | 449 | if colors is not None: 450 | if colors.max() <= 1.0: 451 | colors = (colors * 255).astype(np.uint8) 452 | else: 453 | colors = np.clip(colors, 0, 255).astype(np.uint8) 454 | 455 | if colors.shape[1] == 3: 456 | alpha = np.full((len(vertices), 1), 255, dtype=np.uint8) 457 | colors = np.concatenate([colors, alpha], axis=1) 458 | 459 | header = [ 460 | "ply", 461 | "format binary_little_endian 1.0", 462 | f"element vertex {len(vertices)}", 463 | "property float x", 464 | "property float y", 465 | "property float z", 466 | "property uchar red", 467 | "property uchar green", 468 | "property uchar blue", 469 | "property uchar alpha", 470 | "end_header\n" 471 | ] 472 | header = '\n'.join(header).encode('ascii') 473 | 474 | binary_data = bytearray() 475 | for i in range(len(vertices)): 476 | binary_data.extend(struct.pack(' 0: 530 | mesh_obj = mesh[0] 531 | else: 532 | raise ValueError("Empty mesh list received") 533 | else: 534 | mesh_obj = mesh 535 | 536 | if hasattr(mesh_obj, 'vertices'): 537 | vertices = mesh_obj.vertices 538 | if hasattr(vertices, 'cpu'): 539 | vertices = vertices.cpu() 540 | if hasattr(vertices, 'numpy'): 541 | vertices = vertices.numpy() 542 | vertices = vertices.astype(np.float32) 543 | else: 544 | raise ValueError("Cannot find vertex data in mesh object") 545 | 546 | rotated_vertices = vertices.copy() 547 | rotated_vertices[:, 0] = -vertices[:, 0] 548 | rotated_vertices[:, 1], rotated_vertices[:, 2] = vertices[:, 2], vertices[:, 1] 549 | 550 | final_vertices = rotated_vertices.copy() 551 | final_vertices[:, 0] = rotated_vertices[:, 2] 552 | final_vertices[:, 2] = -rotated_vertices[:, 0] 553 | 554 | vertices = final_vertices 555 | 556 | colors = None 557 | 558 | if hasattr(mesh_obj, 'visual') and mesh_obj.visual is not None: 559 | visual = mesh_obj.visual 560 | 561 | if hasattr(visual, 'vertex_colors') and visual.vertex_colors is not None: 562 | colors = visual.vertex_colors 563 | if hasattr(colors, 'cpu'): 564 | colors = colors.cpu() 565 | if hasattr(colors, 'numpy'): 566 | colors = colors.numpy() 567 | 568 | elif hasattr(visual, 'face_colors') and visual.face_colors is not None: 569 | face_colors = visual.face_colors 570 | if hasattr(face_colors, 'cpu'): 571 | face_colors = face_colors.cpu() 572 | if hasattr(face_colors, 'numpy'): 573 | face_colors = face_colors.numpy() 574 | 575 | if hasattr(mesh_obj, 'faces') and mesh_obj.faces is not None: 576 | faces = mesh_obj.faces 577 | if hasattr(faces, 'cpu'): 578 | faces = faces.cpu() 579 | if hasattr(faces, 'numpy'): 580 | faces = faces.numpy() 581 | 582 | colors = np.zeros((len(vertices), 4), dtype=np.uint8) 583 | vertex_color_count = np.zeros(len(vertices), dtype=np.int32) 584 | 585 | for i, face in enumerate(faces): 586 | for vertex_idx in face: 587 | colors[vertex_idx] += face_colors[i] 588 | vertex_color_count[vertex_idx] += 1 589 | 590 | for i in range(len(vertices)): 591 | if vertex_color_count[i] > 0: 592 | colors[i] = colors[i] // vertex_color_count[i] 593 | 594 | elif hasattr(visual, 'material') and visual.material is not None: 595 | material = visual.material 596 | 597 | if hasattr(material, 'diffuse') and material.diffuse is not None: 598 | diffuse = material.diffuse 599 | colors = np.tile(diffuse, (len(vertices), 1)) 600 | 601 | if colors is None and hasattr(mesh_obj, '_visual') and mesh_obj._visual is not None: 602 | visual = mesh_obj._visual 603 | 604 | if hasattr(visual, 'vertex_colors') and visual.vertex_colors is not None: 605 | colors = visual.vertex_colors 606 | if hasattr(colors, 'cpu'): 607 | colors = colors.cpu() 608 | if hasattr(colors, 'numpy'): 609 | colors = colors.numpy() 610 | 611 | if colors is None and hasattr(mesh_obj, 'visual') and hasattr(mesh_obj.visual, 612 | 'uv') and mesh_obj.visual.uv is not None: 613 | if hasattr(mesh_obj.visual, 'material') and hasattr(mesh_obj.visual.material, 'image'): 614 | uvs = mesh_obj.visual.uv 615 | image = mesh_obj.visual.material.image 616 | 617 | h, w = image.shape[:2] 618 | u = np.clip(uvs[:, 0], 0, 1) * (w - 1) 619 | v = np.clip(uvs[:, 1], 0, 1) * (h - 1) 620 | 621 | u_int = np.round(u).astype(np.int32) 622 | v_int = np.round(v).astype(np.int32) 623 | 624 | u_int = np.clip(u_int, 0, w - 1) 625 | v_int = np.clip(v_int, 0, h - 1) 626 | 627 | colors = image[v_int, u_int] 628 | 629 | if colors is None: 630 | np.random.seed(42) 631 | colors = np.random.randint(100, 200, size=(len(vertices), 3), dtype=np.uint8) 632 | alpha = np.full((len(vertices), 1), 255, dtype=np.uint8) 633 | colors = np.concatenate([colors, alpha], axis=1) 634 | 635 | if colors is not None: 636 | if len(colors.shape) == 1: 637 | colors = np.column_stack([colors, colors, colors]) 638 | 639 | if colors.max() <= 1.0 and colors.dtype != np.uint8: 640 | colors = (colors * 255).astype(np.uint8) 641 | else: 642 | colors = np.clip(colors, 0, 255).astype(np.uint8) 643 | 644 | if colors.shape[1] == 3: 645 | alpha = np.full((len(vertices), 1), 255, dtype=np.uint8) 646 | colors = np.concatenate([colors, alpha], axis=1) 647 | elif colors.shape[1] > 4: 648 | colors = colors[:, :4] 649 | 650 | if len(colors) != len(vertices): 651 | if len(colors) > len(vertices): 652 | colors = colors[:len(vertices)] 653 | else: 654 | last_color = colors[-1] 655 | padding = np.tile(last_color, (len(vertices) - len(colors), 1)) 656 | colors = np.vstack([colors, padding]) 657 | 658 | header = [ 659 | "ply", 660 | "format binary_little_endian 1.0", 661 | f"element vertex {len(vertices)}", 662 | "property float x", 663 | "property float y", 664 | "property float z", 665 | "property uchar red", 666 | "property uchar green", 667 | "property uchar blue", 668 | "property uchar alpha", 669 | "end_header\n" 670 | ] 671 | header = '\n'.join(header).encode('ascii') 672 | 673 | binary_data = bytearray() 674 | for i in range(len(vertices)): 675 | binary_data.extend(struct.pack(' 1: 879 | waveform = waveform[0:1, ...] 880 | waveform = waveform[0] 881 | 882 | with torch.no_grad(): 883 | max_abs = waveform.abs().amax() 884 | if torch.isfinite(max_abs) and max_abs > 1.0: 885 | waveform = waveform / max_abs 886 | 887 | wav_np = waveform.detach().cpu().float().numpy() 888 | if wav_np.ndim != 2: 889 | raise ValueError(f"Waveform must be [C, T] after squeeze, got {wav_np.shape}") 890 | wav_np = np.transpose(wav_np, (1, 0)) 891 | 892 | if format == "wav": 893 | audio_binary, content_type = self._encode_wav(wav_np, sample_rate) 894 | elif format == "mp3": # 改为 mp3 895 | audio_binary, content_type = self._encode_mp3(wav_np, sample_rate, bitrate_kbps) 896 | else: 897 | raise ValueError(f"Unsupported format: {format}") 898 | 899 | return self._send_to_td(audio_binary, content_type, broadcast) 900 | 901 | def _encode_wav(self, wav_np, sample_rate): 902 | import soundfile as sf 903 | buf = io.BytesIO() 904 | if wav_np.ndim == 1: 905 | wav_np = wav_np[:, None] 906 | sf.write(buf, wav_np, samplerate=sample_rate, subtype="PCM_16", format="WAV") 907 | audio_binary = buf.getvalue() 908 | buf.close() 909 | return audio_binary, "audio/wav" 910 | 911 | def _encode_mp3(self, wav_np, sample_rate, bitrate_kbps): 912 | import imageio_ffmpeg, subprocess 913 | with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file: 914 | temp_path = temp_file.name 915 | try: 916 | channels = wav_np.shape[1] if wav_np.ndim == 2 else 1 917 | if wav_np.ndim == 1: 918 | wav_np = wav_np[:, None] 919 | channels = 1 920 | pcm = wav_np.astype(np.float32, copy=False) 921 | if not pcm.flags["C_CONTIGUOUS"]: 922 | pcm = np.ascontiguousarray(pcm) 923 | 924 | cmd = [ 925 | imageio_ffmpeg.get_ffmpeg_exe(), 926 | "-y", 927 | "-f", "f32le", 928 | "-ac", str(channels), 929 | "-ar", str(sample_rate), 930 | "-i", "pipe:0", 931 | "-c:a", "libmp3lame", 932 | "-b:a", f"{int(bitrate_kbps)}k", 933 | temp_path 934 | ] 935 | proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 936 | proc.stdin.write(pcm.tobytes()) 937 | proc.stdin.close() 938 | rc = proc.wait() 939 | if rc != 0: 940 | stderr = proc.stderr.read().decode("utf-8", errors="ignore") 941 | raise RuntimeError(f"ffmpeg failed (code {rc}): {stderr.strip()}") 942 | with open(temp_path, "rb") as f: 943 | audio_binary = f.read() 944 | finally: 945 | try: 946 | os.unlink(temp_path) 947 | except Exception: 948 | pass 949 | return audio_binary, "audio/mpeg" 950 | 951 | def _send_to_td(self, audio_binary, content_type, broadcast): 952 | server = PromptServer.instance 953 | sid = None if broadcast else server.client_id 954 | server.send_sync(1002, audio_binary, sid=sid) 955 | return {"ui": { 956 | "audio": [{ 957 | "source": "websocket", 958 | "content-type": content_type, 959 | "type": "output", 960 | }] 961 | }} 962 | --------------------------------------------------------------------------------